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On November 24, 2023, the Siekopai indigenous population residing in the Ecuadorian Amazon won a claim for the titling of their lands to their Nation. Ecuador recognized indigenous land ownership within a protected area for the first time in history. This decision also acknowledges the value of land management by the indigenous population, who, through their profound knowledge of the place, environment, animals, and spirits that inhabit it, are the best stewards of this green space as a common heritage of humanity. This paper challenges the Western logic that typically drives conservation efforts and examines the role of Indigenous self-determination as a means of ecological advancement. It further outlines the complex role of International mechanisms like Free, Prior, and Informed Consent, ILO Convention 169, and the Rio Declaration as enforcers of sustainable resource management. The court's decision sets a valuable precedent for government collaboration with Indigenous peoples worldwide and the inclusion of Indigenous stewardship practices into environmental justice frameworks. [ABSTRACT FROM AUTHOR]

This paper explores the utilisation of Bayesian structural equation modelling (BSEM) in psychology, highlighting its advantages over frequentist methods for handling complex models and small sample sizes. Basic concepts and fundamental issues relevant to BSEM are introduced, such as prior setting, model convergence, and model fit evaluation and so on. The paper also provides illustrative examples of commonly employed BSEMs, including confirmatory factor analysis (CFA) models, mediation models and multigroup CFA models, accompanied by empirical data and computer codes to facilitate implementation. Our goal is to provide researchers with novel ideas for empirical research and equip them to overcome challenges inherent to traditional methods. As BSEM continues to gain traction in various fields, we anticipate its development will feature improved methods, techniques and reporting standards. [ABSTRACT FROM AUTHOR]

Considering the many titles ascribed to Eckhart – philosopher, theologian, mystic – this paper argues that in his 'Sermons and Lectures on Ecclesiastes' we see that these are all aspects of the Meister which can, and should be, subsumed under the title of Dominican. Eckhart was living out the vision of Dominic, the founder of the Order, that vision of able men who, rooted in the contemplative discipline inherited from Augustine, could articulate the faith in intellectual terms. In his work on Ecclesiastes, he was passionately concerned that those for whom he was responsible should be people of deep faith and integrity as well as intellectually able. [ABSTRACT FROM AUTHOR]

Free, Prior, and Informed Consent (FPIC) is crucial for the exercise of indigenous peoples' right to self-determination, a binding human rights norm, as it provides them with the opportunity to determine how their lands and resources are developed. While numerous companies have committed to respecting FPIC in their corporate policies, there continues to be a huge disconnect between public rhetoric and actions on the ground, and indigenous peoples generally struggle to have a meaningful voice in decision-making processes that concern them. Even if the United Nations Guiding Principles on Business and Human Rights (UNGP, 2011) have compelled companies to gain ground with regards to their responsibility to respect human rights, the 'do-no-harm' principle does not require them to take positive actions towards fulfilling human rights. This approach is inconsistent with the moral foundation of human rights, which implies duties, and does not account for the substantial economic and political power that increases companies' potential as guarantors of human rights. Based on the normative and moral legitimacy sustaining the narrative on corporate human rights obligations and the political, moral and legal imperative behind FPIC, this article asserts that companies have a normative obligation to observe FPIC, which they ought to operationalise in the context of heightened requirements regarding their human rights due diligence. [ABSTRACT FROM AUTHOR]

Estimating correlations among demographic parameters is an important method in population ecology. A recent paper by Deane et al. (Ecology and Evolution 13:e9847, 2023) attempted to explore the effects of different priors for covariance matrices on inference when using mark‐recovery data. Unfortunately, Deane et al. (2023) made a mistake when parameterizing some of their models. Rather than exploring the effects of different priors, they examined the effects of the use of incorrect equations on inference. In this manuscript, we clearly describe the mistake in Deane et al. (2023). We then demonstrate the use of an alternative and appropriate method and reach different conclusions regarding the effects of priors on inference. Consistent with other recent literature, informative inverse Wishart priors can lead to flawed inference, while vague priors on covariance matrix components have little impact when sample sizes are adequate. [ABSTRACT FROM AUTHOR]

This paper studies implications of the consistency conditions among prior, posteriors, and information sets on introspective properties of qualitative belief induced from information sets. The main result reformulates the consistency conditions as: (i) the information sets, without any assumption, almost surely form a partition; and (ii) the posterior at a state is equal to the Bayes conditional probability given the corresponding information set. The main implication of this result is to provide a tractable epistemic model which dispenses with the technical assumptions inherent in the standard epistemic model such as the countable number of information sets. Applications are agreement theorem, no-trade theorem, and the epistemic characterization of correlated equilibria. Implications are as follows. First, since qualitative belief reduces to fully introspective knowledge in the standard environment, a care must be taken when one studies non-veridical belief or non-introspective knowledge. Second, an information partition compatible with the consistency conditions is uniquely determined by the posteriors. Third, qualitative and probability-one beliefs satisfy truth axiom almost surely. The paper also sheds light on how the additivity of the posteriors yields negative introspective properties of beliefs. [ABSTRACT FROM AUTHOR]

For multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence. When preliminary data and likelihood analysis suggest asymmetric tail dependence, a method is proposed to improve extreme value inferences based on the joint lower and upper tails. A prior that uses previous information on tail dependence can be used in combination with the likelihood. With the combination of the prior and the likelihood (which in practice has some degree of misspecification) to obtain a tilted log-likelihood, inferences with suitably transformed parameters can be based on Bayesian computing methods or with numerical optimization of the tilted log-likelihood to obtain the posterior mode and Hessian at this mode. [ABSTRACT FROM AUTHOR]

The present paper considers estimating the shape and scale parameters of the Topp-Leone Exponential distribution. Bayes estimators are obtained using exponential, gamma, log-normal, and Weibull distributions as the identical priors under asymmetric loss functions and integrated with the Lindley approximation method. These priors are compared using Bayes risk through simulation study with varying sample sizes and real data sets. Specifi cally, for the shape parameter of the Topp-Leone exponential distribution, the study identifies that Gamma prior under the Entropy loss function is most preferred. [ABSTRACT FROM AUTHOR]

The present study is an attempt to investigate Doris Lessing's" An Old Woman and her Cat" applying Gerard Genette's model "The Narrative Discourse Method, mainly the concept of voice with its subdivisions of (person or narrator, time of narration, level of narration). The ultimate goal of this research is to spotlight the efficiency of Genette's theory as an analytical tool and to illustrate how this analysis enriches and enhances the readers understanding of Doris Lessing's writing style. The researcher has found out that Genette's model, the concept of "voice" in particular with its sub divisions is applicable to the short story "An Old Woman and her Cat" .Also, the use of narrative technique and stylistic devices contribute to the overall meaning of the short story. Moreover, the narrative choices influence the readers' perception of characters, events, and themes within the short story. [ABSTRACT FROM AUTHOR]

The prior plays a central role in Bayesian inference but specifying a prior is often difficult and a prior considered appropriate by a modeler may be significantly biased. We propose multi-pass Bayesian estimation (MBE), a robust Bayesian method capable of adjusting the prior's influence on the inference result based on the prior's quality. MBE adjusts the relative importance of the prior and the data by iteratively performing approximate Bayesian updates on the given data, with the number of updates determined using a cross-validation method. The repeated use of the data resembles the data cloning method, but data cloning performs maximum likelihood estimation (MLE), while MBE interpolates between standard Bayesian inference and MLE; there are also algorithmic differences in how MBE and data cloning make repeated use of the data. Alternatively, MBE can be considered a method for constructing a new prior from the given initial prior and the data. We additionally provide a new non-asymptotic bound on the convergence of data cloning, and provide an MBE-like iterative heuristic approach which achieves faster convergence speed by boosting posterior variance. In numerical simulations on several simulated and real-world datasets, MBE provides robust inference results as compared to standard Bayesian inference and MLE. [ABSTRACT FROM AUTHOR]

The paper introduces a Bayesian approach for estimating parameters of the Chris-Jerry distribution, focusing on the use of a conjugate prior, specifically the gamma prior. The Bayesian estimation method is developed with a various loss function, offering a robust framework for parameter estimation. symmetric loss function and Linex loss functions are commonly used in Bayesian statistics to balance the trade-off between bias and variance. The central idea is to derive the Bayes estimate of the distribution parameter by leveraging the properties of the conjugate gamma prior. Conjugate priors simplify the Bayesian analysis by ensuring that the posterior distribution belongs to the same family as the prior, facilitating analytical calculations. The proposed methodology is implemented and validated through numerical illustrations using. This involves applying the developed Bayesian estimation framework to real-world data or simulated scenarios, demonstrating its effectiveness and practical applicability. The numerical and simulation studies are done by using r software [ABSTRACT FROM AUTHOR]

We present a Non-Rigid Structure-from-Motion (NRSfM) method to reconstruct an object whose topology is known. We represent the topology by a 3D shape that weakly resembles the object, which we call a Topological Template (ToTem). The ToTem has two main differences with the template used in Shape-from-Template (SfT). First, the shape in the ToTem is not necessarily feasible for the object, whereas it must be in the SfT template. Second, the ToTem only models shape, excluding the classical texture-map representing colour in the SfT template. These two differences greatly alleviate the practical difficulty of constructing a template. However, they make the reconstruction problem challenging, as they preclude the use of strong deformation constraints between the template shape and the reconstruction and the possibility of directly establishing correspondences between the template and the images. Our method uses an isometric deformation prior and proceeds in four steps. First, it reconstructs point-clouds from the images. Second, it aligns the ToTem to the point-clouds. Third, it creates a coherent surface parameterisation. Fourth, it performs a global refinement, posed as Bundle Adjustment (BA). We show experimentally that our method outperforms the existing methods for its isolated steps and NRSfM methods overall, in terms of 3D accuracy, ability to reconstruct the object's visible surface and ability to approximate the object's invisible surface. [ABSTRACT FROM AUTHOR]

This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality, sample size, model size, the magnitude of misspecified path effect, and the choice or prior on the performance of the fit indices was also investigated. The Bayesian fit indices examined in this study included PPP, DIC, BRMSEA, BCFI, BTLI. The results from the simulation study showed that BRMSEA, BCFI, and BTLI failed to detect structural misspecification. The performance of DIC depended majorly on measurement quality, and the sensitivity of PPP depended on sample size, measurement quality, the magnitude of the omitted path effect, and the choice of prior. Informative prior with too narrow precisions resulted in higher FP rates. Empirical implications for applied researchers and future research directions were discussed. [ABSTRACT FROM AUTHOR]

Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin‐targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top‐down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression. [ABSTRACT FROM AUTHOR]

Autistic people exhibit atypical use of prior information when processing simple perceptual stimuli; yet, it remains unclear whether and how these difficulties in using priors extend to complex social stimuli. Here, we compared autistic people without accompanying intellectual disability and nonautistic people in their ability to acquire an "emotional prior" of a facial expression and update this prior to a different facial expression of the same identity. Participants performed a two‐interval same/different discrimination task between two facial expressions. To study the acquisition of the prior, we examined how discrimination was modified by the contraction of the perceived facial expressions toward the average of presented stimuli (i.e., regression to the mean). At first, facial expressions surrounded one average emotional prior (mostly sad or angry), and then the average switched (to mostly angry or sad, accordingly). Autistic people exhibited challenges in facial discrimination, and yet acquired the first prior, demonstrating typical regression‐to‐the‐mean effects. However, unlike nonautistic people, autistic people did not update their perception to the second prior, suggesting they are less flexible in updating an acquired prior of emotional expressions. Our findings shed light on the perception of emotional expressions, one of the most pressing challenges in autism. Lay Summary: The ability to flexibly adapt to changing emotional context is critical for social interactions and is one of the most pressing challenges in social functioning of autistic people. For instance, a mother of an autistic child, who is angry at her son, even after the anger has passed, may hear her son asking, "Are you still angry?", reflecting the importance of understanding, adapting, and conveying her emotional state and the level of uncertainty in the child's own ability to read out the change of her emotional state. Although the perception of facial expressions is difficult for autistic people, it is unclear how this difficulty may impact the ability to adapt to changing emotional context. Here, we examined this ability in autistic and nonautistic people, specifically testing how discrimination was modified by the emotional context formed by the presented facial expressions. First, participants were exposed to expressions surrounding one emotion (e.g., mostly angry expressions). Then, the facial expressions shifted to surround a different emotion (e.g., to mostly sad expressions). We show that although autistic people exhibit difficulties in discriminating between facial expressions, the perception of both groups was influenced to a similar degree by the exposure to the first emotional context. But when the emotional context shifted, autistic people demonstrated difficulty adjusting to the change. Our results suggest that autistic people are characterized by general inflexibility in adjusting to changes, which likely contributes to their difficulties in social interaction. [ABSTRACT FROM AUTHOR]

This article examines the interplay between local activism and external and elite actors in response to ReconAfrica's oil and gas exploration activities in Namibia's Kavango and Botswana's Okavango Delta regions. The research demonstrates local communities' challenges with confronting environmental degradation, unfulfilled promises, and legal transgressions while striving to protect their ancestral lands and rights. The study, grounded in Southern green criminology, highlights the power imbalances between Northern corporations and Southern territories, underscoring the exploitation and marginalisation of Indigenous Peoples and local communities. It advocates for genuine collaboration and prioritising local perspectives in environmental activism, calling for a shift in power dynamics to ensure equitable and sustainable outcomes. This cooperative model integrates local knowledge systems, amplifies Indigenous voices, and aligns environmental campaigns with broader social justice concerns. The traditional externally driven approaches are challenged, while ethical and inclusive environmental activism is also promoted. [ABSTRACT FROM AUTHOR]

Bayesian decision theory suggests that optimal decision-making should use and weigh prior beliefs with current information, according to their relative uncertainties. However, some characteristics of borderline personality disorder (BPD) patients, such as fast, drastic changes in the overall perception of themselves and others, suggest they may be underrelying on priors. Here, we investigated if BPD patients have a general deficit in relying on or combining prior with current information. We analyzed this by having BPD patients (n = 23) and healthy controls (n = 18) perform a coin-catching sensorimotor task with varying levels of prior and current information uncertainty. Our results indicate that BPD patients learned and used prior information and combined it with current information in a qualitatively Bayesian-like way. Our results show that, at least in a lower-level, nonsocial sensorimotor task, BPD patients can appropriately use both prior and current information, illustrating that potential deficits using priors may not be widespread or domain-general. [ABSTRACT FROM AUTHOR]

Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationally efficient sensitivity analysis approach using importance sampling to estimate properties of posteriors resulting from power-scaling the prior or likelihood. On this basis, we suggest a diagnostic that can indicate the presence of prior-data conflict or likelihood noninformativity and discuss limitations to this power-scaling approach. The approach can be easily included in Bayesian workflows with minimal effort by the model builder and we present an implementation in our new R package priorsense. We further demonstrate the workflow on case studies of real data using models varying in complexity from simple linear models to Gaussian process models. [ABSTRACT FROM AUTHOR]

Capturing finer and discriminative difference features (DFs) is key to obtaining a high-quality change detection (CD) map. However, there is still significant scope for further study on fine-grained detection, especially concerning terms of improving structural integrity and reducing internal holes or sticking in DF. To this end, we propose a progressive difference amplification network (PDANet) with edge sensitivity to detect changed areas in optical remote sensing images (RSIs), where the key point is to amplify DF and reinforce edge detail to improve CD accuracy. The edge sensitivity (ES) encoder is designed to capture the long-distance dependency, which compensates for the limited receptive fields of the convolutional neural network with fixed kernels. Meanwhile, we introduce the prior edge in the network training stage, which collaborates with the ESE to improve the structural integrity of the changed areas. On the other hand, the difference amplification decoder is proposed to enhance the representation of the changed areas, and it is achieved by integrating multiscale DF and reconstructing the original single RSI using DF as full-stage guidance. Finally, the CD map and edge map are predicted based on the reconstructed feature and the maximum scale DF. Extensive experiments on one instance dataset and three CD benchmark datasets demonstrate that PDANet outperforms the state-of-the-art CD competitors both qualitatively and quantitatively.

Simple Summary: This paper provides a review of statistical methods for tumor-agnostic clinical trials. In particular, the review focuses on basket trials and provides methodological insights into various Bayesian approaches. The key concept of borrowing information through Bayesian hierarchical models is emphasized, and some novel trial designs are introduced. The review is expected to provide oncology and biostatistics researchers with more exposure to powerful Bayesian methods for the design and analysis of tumor-agnostic clinical trials. Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings. [ABSTRACT FROM AUTHOR]

We introduce Bayesian reasoning in court not as a toolbox for doing computations, but as a way to assess evidence in a case. We argue that Bayesian reasoning comes naturally, even when the findings in a case cannot readily be translated into numbers. Not having numbers at one's disposal is not an obstacle to use Bayesian reasoning. Although we present a coherent and complete view, we focus on the prior, since that seems to be the most problematic part of Bayesian reasoning. We explain that attempts to numerically express the prior fail in general, but also that a prior is necessary and cannot be dispensed with. Indeed, we explain in detail why decision-making should not be based on likelihood ratios alone. We next discuss two of the most delicate questions around the prior: (1) the possible conflict with the presumption of innocence, and (2) the idea that unwanted personal conviction (like racism) might enter the decision procedure via the prior. We conclude that these alleged problems are not problematic after all, and we carefully explain this position. [ABSTRACT FROM AUTHOR]

In recent decades, funding agencies, universities and research institutes have imposed written forms where researchers declare to have the free and informed consent and/or endorsements of the indigenous people, communities or institutions with whom they are going to develop their anthropological work. This is in compliance with National Law No. 24071 which approves Convention 169 of the International Organization on Indigenous and Tribal Peoples in independent countries. Accordingly, codes of professional ethics have been written and different ethics committees have been created to regulate and endorse these scientific and academic practices. The narration and reflection on the ethnographic situations experienced together with members of indigenous communities in the Province of Córdoba (Argentina) in response to the request for endorsement for university extension scholarships, the request for consent to write and publish a book about their ancestors, the holding of a discussion and the collective writing of notes for the website of the Museum of Anthropology in tha Faculty of Philosophy and Humanities at Universidad Nacional de Córdoba, are presented in these pages. The objective is to change “an additional obligation, an additional requirement” into an object of study, taking into account the intercultural context in which researchers find themselves. [ABSTRACT FROM AUTHOR]

The world surrounding us possesses temporally and spatially organized structures deriving from various physical laws. By leveraging these spatiotemporal structures of the external environment as prior information, the visual system compensates for uncertain and unreliable sensory information, facilitating efficient and adaptive perceptual processing. The serial dependence in visual perception refers to the phenomenon where the perception of current stimulus is biased toward previously seen stimuli, suggesting that the brain interprets a series of similar events occurring in succession as continuous events with the same origin. In this paper, we introduce our recent research on serial dependence and discuss its individual differences, and discuss how each individual acquires statistical information in the environment and to what extent they can utilize the acquired prior information. [ABSTRACT FROM AUTHOR]

Inferences about hypotheses are ubiquitous in the cognitive sciences. Bayes factors provide one general way to compare different hypotheses by their compatibility with the observed data. Those quantifications can then also be used to choose between hypotheses. While Bayes factors provide an immediate approach to hypothesis testing, they are highly sensitive to details of the data/model assumptions and it's unclear whether the details of the computational implementation (such as bridge sampling) are unbiased for complex analyses. Here, we study how Bayes factors misbehave under different conditions. This includes a study of errors in the estimation of Bayes factors; the first-ever use of simulation-based calibration to test the accuracy and bias of Bayes factor estimates using bridge sampling; a study of the stability of Bayes factors against different MCMC draws and sampling variation in the data; and a look at the variability of decisions based on Bayes factors using a utility function. We outline a Bayes factor workflow that researchers can use to study whether Bayes factors are robust for their individual analysis. Reproducible code is available from https://osf.io/y354c/. In psychology and related areas, scientific hypotheses are commonly tested by asking questions like "is [some] effect present or absent." Such hypothesis testing is most often carried out using frequentist null hypothesis significance testing (NHST). The NHST procedure is very simple: It usually returns a p-value, which is then used to make binary decisions like "the effect is present/absent." For example, it is common to see studies in the media that draw simplistic conclusions like "coffee causes cancer," or "coffee reduces the chances of getting cancer." However, a powerful and more nuanced alternative approach exists: Bayes factors. Bayes factors have many advantages over NHST. However, for the complex statistical models that are commonly used for data analysis today, computing Bayes factors is not at all a simple matter. In this article, we discuss the main complexities associated with computing Bayes factors. This is the first article to provide a detailed workflow for understanding and computing Bayes factors in complex statistical models. The article provides a statistically more nuanced way to think about hypothesis testing than the overly simplistic tendency to declare effects as being "present" or "absent.". [ABSTRACT FROM AUTHOR]

In recent years, some researchers have put forth the compactness hypothesis, which suggests that similar colours tend to accumulate in the salient region as opposed to the non-salient region in image saliency detection. We discovered that the k-nearest neighbour (kNN) mechanism assumes the presence of similar objects in close proximity. As the kNN method is a supervised learning approach, we introduced an unfixed k value and combined it with the clustering idea of k-means to develop a novel algorithm called kNN clustering. We proposed an object-biased prior and an improved boundary and background prior based on a given compact matrix. Our algorithm was extensively tested on five publicly available datasets. The experimental results demonstrated that it outperformed eight existing top unsupervised models in producing high-quality saliency maps at full resolution. The improved prior methods were particularly effective when employed with existing algorithms lacking prior knowledge, especially with low-performing models. [ABSTRACT FROM AUTHOR]

Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai'i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model's unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a new fuzzy regression model based on a common Bayesian nonparametric-based method with exact predictors and fuzzy responses. To this end, the left, center, and right values of the unknown fuzzy smooth function were evaluated based on the conventional kernel-based Bayesian method adopted with Gaussian kernel and a multivariate normal distribution as a prior distribution function in the cases where the residuals were assumed to be observed values of a normal distribution function. The unknown components of the model including bandwidth and variance were estimated via a hybrid algorithm. In this regard, a generalized cross-validation and similarity measure between two LR-fuzzy numbers were applied. The mean similarity measure criterion and a mean square error were utilized to assess the performance of the proposed method. Some applied examples and comparison studies were considered to clarify the proposed method and illustrate its performance relative to some common fuzzy nonlinear methods. The results indicated the superior performance of the proposed method over other nonlinear methods. Further, two essential assumptions associated with proposed regression models including homoscedasticity and normality of residuals were also analyzed in each example based on some common scatter plots and a well-established hypothesis test. [ABSTRACT FROM AUTHOR]

Knowledge without awareness, or implicit knowledge, influences a variety of behaviors. It is unknown however, whether implicit knowledge of statistical structure informs visual perceptual decisions or whether explicit knowledge of statistical probabilities is required. Here, we measured visual decision-making performance using a novel task in which humans reported the orientation of two differently colored translational Glass patterns; each color associated with different orientation probabilities. The task design allowed us to assess participants’ ability to learn and use a general orientation prior as well as a color specific feature prior. Classifying decision-makers based on a questionnaire revealed that both implicit and explicit learners implemented a general orientation bias by adjusting the starting point of evidence accumulation in the drift diffusion model framework. Explicit learners additionally adjusted the drift rate offset. When subjects implemented a stimulus specific bias, they did so by adjusting primarily the drift rate offset. We conclude that humans can learn priors implicitly for perceptual decision-making and depending on awareness implement the priors using different mechanisms.

Objective: The main aim of this study is to derive the alternative risk and loss function for Bayesian paradigm. Bayesian Decision making is an integral part of Bayesian inference which had for long overshadowed due to the adoption of frequentist performance metric, and Bayesian inference is probabilistic in nature, treating it in classical paradigm would lead to poor performances. This study seeks to examine critically and introduce weight which eventually makes Bayesian estimates compare favourably well with classical estimates. Material and Methods: Balanced weighted precision loss function was adopted and described; it is an extraction of precision of estimates from balanced loss function which ordinarily combined goodness of fit and precision of estimates. The goodness of fit criterion measures the quality of data while the precision of estimates measures the quality of inferences, combining the two criteria may lead to loss of information as each criterion has its specific role in both classical and Bayesian paradigms. Results: Weighted quadratic loss to measure the precision of estimates of Posterior mean and Bayes estimate were constructed as a standard metric. The study established the estimation characteristics under weighted quadratic loss function which makes Bayesian inference compare favourably well with other estimators. Conclusion: It is therefore recommended that weighted quadratic loss function of assessment criteria of both posterior mean and Bayes estimates is of importance for correct comparison. [ABSTRACT FROM AUTHOR]

I argue that 'conspiracy theory' and 'conspiracy theorist' as commonly employed are 'tonkish' terms (as defined by Arthur Prior and Michael Dummett), licensing inferences from truths to falsehoods; indeed, that they are mega-tonkish terms, since their use is governed by different and competing sets of introduction and elimination rules, delivering different and inconsistent results. Thus 'conspiracy theory' and 'conspiracy theorist' do not have determinate extensions, which means that generalizations about conspiracy theories or conspiracy theorists do not have determinate truth-values. Hence conspiracy theory theory – psychological or social scientific research into conspiracy theorists and what is wrong with them – is often about as intellectually respectable as an enquiry into bastards and what makes them so mean. [ABSTRACT FROM AUTHOR]

Uncertain or indirect “soft” data, such as geologic interpretation, driller’s logs, geophysical logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data “hardness” parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or “prior realization”) can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

Patients on extracorporeal membrane oxygenation (ECMO) often require prolonged periods of bed rest owing to the severity of their illness. Care is also required to maintain the position and integrity of the ECMO cannula. However, they experience a range of effects due to prolonged bed rest. This systematic review examined the possible effects of the early mobilization in patients on ECMO. The database PUBMED was searched by using appropriate keywords: “rehabilitation”, “mobilization”, “ECMO” and “extracorporeal membrane oxygenation”. The selection criteria for the article search were the following: (a) studies published in the last five years, (b) descriptive studies, (c) randomized studies, (d) published in the English language and (e) studies in adults. A total of 259 studies were found, 8 of which were finally selected. Most of the studies showed that early intensive physical rehabilitation related to a decrease in in-hospital stay and a reduction in the duration of mechanical ventilation and doses of vasopressors. In addition, improvements in the functional status and rate of mortality were observed along with a reduction in health care costs. Exercise training should be a fundamental part of the management of patients on ECMO.

Large-scale tests often contain mixed-format items, such as when multiple-choice (MC) items and constructed-response (CR) items are both contained in the same test. Although previous research has analyzed both types of items simultaneously, this may not always provide the best estimate of ability. In this paper, a two-step sequential Bayesian (SB) analytic method under the concept of empirical Bayes is explored for mixed item response models. This method integrates ability estimates from different item formats. Unlike the empirical Bayes method, the SB method estimates examinees' posterior ability parameters with individual-level sample-dependent prior distributions estimated from the MC items. Simulations were used to evaluate the accuracy of recovery of ability and item parameters over four factors: the type of the ability distribution, sample size, test length (number of items for each item type), and person/item parameter estimation method. The SB method was compared with a traditional concurrent Bayesian (CB) calibration method, EAPsum, that uses scaled scores for summed scores to estimate parameters from the MC and CR items simultaneously in one estimation step. From the simulation results, the SB method showed more accurate and reliable ability estimation than the CB method, especially when the sample size was small (150 and 500). Both methods presented similar recovery results for MC item parameters, but the CB method yielded a bit better recovery of the CR item parameters. The empirical example suggested that posterior ability estimated by the proposed SB method had higher reliability than the CB method. [ABSTRACT FROM AUTHOR]

This study was conducted to test the significance of adding row and column factors in the frequentist and Bayesian models used in the evaluation of a population of Passiflora edulis, as well as selecting promising genotypes to form the next generation. The following parameters were evaluated: number of fruits, yield, fruit weight, transverse fruit diameter, longitudinal fruit diameter, pulp percentage, skin thickness and total soluble solids. For the Bayesian model, two priors were considered, namely, inverse gamma and a priori distribution with extended parameters. The model with a priori distribution with extended parameters showed lower root mean square error and higher correlation coef- ficient between observed and predicted values than the inverse gamma model. Furthermore, for a selection intensity of 37%, the mixed and Bayesian models selected practically the same progenies in both experiments. The use of the 5-fold cross-validation technique indicated that both tested models were efficient. [ABSTRACT FROM AUTHOR]

Patients on extracorporeal membrane oxygenation (ECMO) often require prolonged periods of bed rest owing to the severity of their illness. Care is also required to maintain the position and integrity of the ECMO cannula. However, they experience a range of effects due to prolonged bed rest. This systematic review examined the possible effects of the early mobilization in patients on ECMO. The database PUBMED was searched by using appropriate keywords: "rehabilitation", "mobilization", "ECMO" and "extracorporeal membrane oxygenation". The selection criteria for the article search were the following: (a) studies published in the last five years, (b) descriptive studies, (c) randomized studies, (d) published in the English language and (e) studies in adults. A total of 259 studies were found, 8 of which were finally selected. Most of the studies showed that early intensive physical rehabilitation related to a decrease in in-hospital stay and a reduction in the duration of mechanical ventilation and doses of vasopressors. In addition, improvements in the functional status and rate of mortality were observed along with a reduction in health care costs. Exercise training should be a fundamental part of the management of patients on ECMO. [ABSTRACT FROM AUTHOR]

Uncertain or indirect “soft” data, such as geologic interpretation, driller’s logs, geophysical logs or imaging, offer potential constraints or “soft conditioning” to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data “hardness” parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or “prior realization”) can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

Bayesian statistics offers a normative description for how a person should combine their original beliefs (i.e., their priors) in light of new evidence (i.e., the likelihood). Previous research suggests that people tend to under-weight both their prior (base rate neglect) and the likelihood (conservatism), although this varies by individual and situation. Yet this work generally elicits people's knowledge as single point estimates (e.g., x has a 5% probability of occurring) rather than as a full distribution. Here we demonstrate the utility of eliciting and fitting full distributions when studying these questions. Across three experiments, we found substantial variation in the extent to which people showed base rate neglect and conservatism, which our method allowed us to measure for the first time simultaneously at the level of the individual. While most people tended to disregard the base rate, they did so less when the prior was made explicit. Although many individuals were conservative, there was no apparent systematic relationship between base rate neglect and conservatism within each individual. We suggest that this method shows great potential for studying human probabilistic reasoning.

Underwater images play a crucial role in various fields, including oceanographic engineering, marine exploitation, and marine environmental protection. However, the quality of underwater images is often severely degraded due to the complexities of the underwater environment and equipment limitations. This degradation hinders advancements in relevant research. Consequently, underwater image restoration has gained significant attention as a research area. With the growing interest in deep-sea exploration, deep-sea image restoration has emerged as a new focus, presenting unique challenges. This paper aims to conduct a systematic review of underwater image restoration technology, bridging the gap between shallow-sea and deep-sea image restoration fields through experimental analysis. This paper first categorizes shallow-sea image restoration methods into three types: physical model-based methods, prior-based methods, and deep learning-based methods that integrate physical models. The core concepts and characteristics of representative methods are analyzed. The research status and primary challenges in deep-sea image restoration are then summarized, including color cast and blur caused by underwater environmental characteristics, as well as insufficient and uneven lighting caused by artificial light sources. Potential solutions are explored, such as applying general shallow-sea restoration methods to address color cast and blur, and leveraging techniques from related fields like exposure image correction and low-light image enhancement to tackle lighting issues. Comprehensive experiments are conducted to examine the feasibility of shallow-sea image restoration methods and related image enhancement techniques for deep-sea image restoration. The experimental results provide valuable insights into existing methods for addressing the challenges of deep-sea image restoration. An in-depth discussion is presented, suggesting several future development directions in deep-sea image restoration. Three main points emerged from the research findings: i) Existing shallow-sea image restoration methods are insufficient to address the degradation issues in deep-sea environments, such as low-light and uneven illumination. ii) Combining imaging physical models with deep learning to restore deep-sea image quality may potentially yield desirable results. iii) The application potential of unsupervised and zero-shot learning methods in deep-sea image restoration warrants further investigation, given their ability to work with limited training data.

Alethic pluralists often claim that accommodating certain alethic platitudes motivates rejecting deflationism in favour of a pluralist inflationism about truth. Deflationists claim that the logical role of the truth predicate, viz providing something equivalent to variables for sentence-in-use positions and quantifiers governing them, is sufficient to account for the appeal to truth in the alethic platitudes. Surprisingly, however, most deflationists face an insufficiently acknowledged problem with respect to explaining how this mode of generalizing works. The standard substitutional or higher-order interpretations of sentential quantifiers and variables do not meet two desiderata that we claim any adequate account of them must satisfy. To address this issue, we review and extend A. N. Prior's adverbial understanding of sentential quantification, explain how it satisfies the desiderata, and respond to some objections. This shows that deflationists can accommodate and account for the alethic platitudes by applying this non-nominal understanding of generalizing on sentence positions. [ABSTRACT FROM AUTHOR]

Aim: Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus‐azole antifungal interaction. Methods: Data from heart transplant recipients (n = 87) administered the oral immediate‐release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka, V2/F, Q/F and V3/F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat‐free mass was implemented as a covariate on CL/F, V2/F, V3/F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction‐corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions. Results: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = −44, P <.001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing. Conclusion: A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus–azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Complex models in physics, biology, economics, and engineering are often sloppy, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce the model manifold of predictions, whose coordinates are the model parameters. Its hyperribbon structure explains why only a few parameter combinations matter for the behavior. We review recent rigorous results that connect the hierarchy of hyperribbon widths to approximation theory, and to the smoothness of model predictions under changes of the control variables. We discuss recent geodesic methods to find simpler models on nearby boundaries of the model manifold—emergent theories with fewer parameters that explain the behavior equally well. We discuss a Bayesian prior which optimizes the mutual information between model parameters and experimental data, naturally favoring points on the emergent boundary theories and thus simpler models. We introduce a 'projected maximum likelihood' prior that efficiently approximates this optimal prior, and contrast both to the poor behavior of the traditional Jeffreys prior. We discuss the way the renormalization group coarse-graining in statistical mechanics introduces a flow of the model manifold, and connect stiff and sloppy directions along the model manifold with relevant and irrelevant eigendirections of the renormalization group. Finally, we discuss recently developed 'intensive' embedding methods, allowing one to visualize the predictions of arbitrary probabilistic models as low-dimensional projections of an isometric embedding, and illustrate our method by generating the model manifold of the Ising model. [ABSTRACT FROM AUTHOR]

Given a squared Euclidean norm penalty, we examine some less well-known properties of shrinkage estimates. In particular, we highlight that it is possible for some components of the shrinkage estimator to be placed further away from the prior mean than the original estimate. An analysis of this effect is provided within three different modeling settings—encompassing linear, logistic, and ordinal regression models. Additional simulations show that the outlined effect is not a mathematical artefact, but likely to occur in practice. As a byproduct, they also highlight the possibilities of sign reversals ("overshoots") for shrinkage estimates. We point out practical consequences and challenges, which might arise from the observed effects with special emphasis on psychometrics. [ABSTRACT FROM AUTHOR]

Dreams are still an enigma of human cognition, studied extensively in psychoanalysis and neuroscience. According to the Freudian dream theory and Solms' modifications of the unconscious derived from it, the fundamental task of meeting our emotional needs is guided by the principle of homeostasis. Our innate value system generates conscious feelings of pleasure and unpleasure, resulting in the behavior of approaching or withdrawing from the world of objects. Based on these experiences, a hierarchical generative model of predictions (priors) about the world is constantly created and modified, with the aim to optimize the meeting of our needs by reducing prediction error, as described in the predictive processing model of cognition. Growing evidence from neuroimaging supports this theory. The same hierarchical functioning of the brain is in place during sleep and dreaming, with some important modifications like a lack of sensual and motor perception and action. Another characteristic of dreaming is the predominance of primary process thinking, an associative, non-rational cognitive style, which can be found in similar altered states of consciousness like the effect of psychedelics. Mental events that do not successfully fulfill an emotional need will cause a prediction error, leading to conscious attention and adaptation of the priors that incorrectly predicted the event. However, this is not the case for repressed priors (RPs), which are defined by the inability to become reconsolidated or removed, despite ongoing error signal production. We hypothesize that Solms' RPs correspond with the conflictual complexes, as described by Moser in his dream formation theory. Thus, in dreams and dream-like states, these unconscious RPs might become accessible in symbolic and non-declarative forms that the subject is able to feel and make sense of. Finally, we present the similarities between dreaming and the psychedelic state. Insights from psychedelic research could be used to inform dream research and related therapeutic interventions, and vice versa. We propose further empirical research questions and methods and finally present our ongoing trial “Biological Functions of Dreaming” to test the hypothesis that dreaming predicts intact sleep architecture and memory consolidation, via a lesion model with stroke patients who lost the ability to dream.

In this paper, a set of important objective priors are examined for the Bayesian estimation of the parameters present in the Poisson-Exponential distribution P E. We derived the multivariate Jeffreys prior and the Maximal Data Information Prior. Reference prior and others priors proposed in the literature are also analyzed. We show that the posterior densities resulting from these approaches are proper although the respective priors are improper. Monte Carlo simulations are used to compare the efficiencies and to assess the sensitivity of the choice of the priors, mainly for small sample sizes. This simulation study shows that the mean square error, mean bias and coverage probability of credible intervals under Gamma, Jeffreys' rule and Box & Tiao priors presented equal results, whereas Jeffreys and Reference priors showed the best results. The MDIP prior had a worse performance in all analyzed situations showing not to be indicated for Bayesian analysis of the P E distribution. A real data set is analyzed for illustrative purpose of the Bayesian approaches. [ABSTRACT FROM AUTHOR]

The prevalence of depressive symptoms decreases from late adolescence to middle age adulthood. Furthermore, despite significant losses in motor and cognitive functioning, overall emotional well-being tends to increase with age, and a bias to positive information has been observed multiple times. Several causes have been discussed for this age-related development, such as improvement in emotion regulation, less regret, and higher socioeconomic status. Here, we explore a further explanation. Our minds host mental models that generate predictions about forthcoming events to successfully interact with our physical and social environment. To keep these models faithful, the difference between the predicted and the actual event, that is, the prediction error, is computed. We argue that prediction errors are attenuated in the middle age and older mind, which, in turn, may translate to less negative affect, lower susceptibility to affective disorders, and possibly, to a bias to positive information. Our proposal is primarily linked to perceptual inferences, but may hold as well for higher-level, cognitive, and emotional forms of error processing. [ABSTRACT FROM AUTHOR]