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The system considered in this paper operates with a feedback that is characterized by a gain and a desired final state (DFS), which is the main parameter of interest in the present study. The system is, however, subjected intermittently to stochastic inputs according to a Markov process. Since the system operates in two modes | under the feedback to the DFS and under a stochastic input|the Interacting Multiple Model (IMM) estimator is used. Two approaches are considered: (i) the DFS is a discrete-valued random variable | one of a finite number of possible states | with an a priori probability mass function (pmf), and (ii) the DFS is a continuous-valued random variable with an a priori probability density function (pdf). For Approach (i), we use a multiple IMM estimator (MIMM) that features one IMM for each one of the possible DFS. The a posteriori probability of the model for each IMM, i.e., of each DFS, will be computed based on the likelihood function (LF) of the corresponding IMM. For Approach (ii), we design a single IMM to handle the unknown DFS to be estimated (mode M1), and the random inputs (mode M2). Simulation results explore several scenarios and investigate the degree of observability of this stochastic problem.

Many researchers have used sound sensors to record audio data from insects, and used these data as inputs of machine learning algorithms to classify insect species. In image classification, the convolutional neural network (CNN), a well-known deep learning algorithm, achieves better performance than any other machine learning algorithm. This performance is affected by the characteristics of the convolution filter (ConvFilter) learned inside the network. Furthermore, CNN performs well in sound classification. Unlike image classification, however, there is little research on suitable ConvFilters for sound classification. Therefore, we compare the performances of three convolution filters, 1D-ConvFilter, 3×1 2D-ConvFilter, and 3×3 2D-ConvFilter, in two different network configurations, when classifying mosquitoes using audio data. In insect sound classification, most machine learning researchers use only audio data as input. However, a classification model, which combines other information such as activity circadian rhythm, should intuitively yield improved classification results. To utilize such relevant additional information, we propose a method that defines this information as a priori probabilities and combines them with CNN outputs. Of the networks, VGG13 with 3×3 2D-ConvFilter showed the best performance in classifying mosquito species, with an accuracy of 80.8%. Moreover, adding activity circadian rhythm information to the networks showed an average performance improvement of 5.5%. The VGG13 network with 1D-ConvFilter achieved the highest accuracy of 85.7% with the additional activity circadian rhythm information.

The channel capacity of noncoherent reception of multi-level one-sided amplitude-shift keying (ASK), which is an asymmetric constellation, in Rayleigh fading with receive diversity and energy detection is considered. The asymmetries result in capacity achieving input probability distributions, that is, a priori probability distributions, that deviate from uniformity. An analytical expression for the mutual information in terms of a single integral is derived, and from it the set of equations, which can be solved to obtain the optimum or capacity achieving input probabilities, is obtained. High and low signal-to-noise ratio (SNR) approximations of the optimum input probabilities and the capacity are derived next. Furthermore, a logarithmic upper bound on the mutual information is obtained. Numerical results confirm that the uniform distribution of input probabilities is not capacity achieving. For example, with average SNR per symbol per branch of 6 dB, the relative deviation of the mutual information (with uniform input distribution) from the capacity is nearly 20% for 4-level ASK with one transmit diversity branch and two receive diversity branches. Furthermore, the derived high and low SNR approximations to the capacity are shown to be reasonably accurate.

Предметом вивчення в статті є інформаційна технологія обробки використання геопросторових інформаційних структур. Метою є удосконалення інформаційної технології обробки використання геопросторових інформаційних структур. Використовуваними методами є: методи теорії імовірності, математичної статистики, методи оптимізації, математичного моделювання та цифрової обробки зображень, аналітичні та емпіричні методи порівняльного дослідження. Отримані такі результати. В IDEF0-нотації описані етапи основного процесу прийняття управлінських рішень на основі аналізу геопросторової інформації. Наведена декомпозиція основного процесу прийняття управлінських рішень на основі аналізу геопросторової інформації, декомпозиція підпроцесу проведення тематичної інтерпретації видових зображень інформації, декомпозиція підпроцесу оцінювання апріорних імовірностей, декомпозиція підпроцесу прогнозування апріорних імовірностей та декомпозиція підпроцесу вибору оптимального рішення. Висновки. Удосконалено інформаційну технологію використання геопросторових інформаційних структур, яка, на відміну від відомих, базується на розроблених моделях формування видових зображень, тематичної інтерпретації видових зображень, прогнозування геопросторових даних та прийняття управлінських рішень і методах семантичної сегментації видових зображень та прийняття управлінських рішень на основі аналізу геопросторової інформації, що дозволяє формалізувати та автоматизувати процеси аналізу геопросторових даних, задовільнити зростаючі вимоги до систем обробки геопросторової інформації в умовах ризику та невизначеності та дозволяє приймати на їх основі обґрунтовані управлінські рішення.

In the presence of random and interval hybrid uncertainty (RI-HU), the safety degree of the structure system can be quantified by the upper and lower bounds of failure probability. However, there is a lack of efficient methods for estimating failure probability under RI-HU in present. Therefore, a novel method is proposed in this paper. In the proposed method, the interval variables are extended to the random variables by assigning a priori probability density function, in which the conditional density estimation (CDE)–based method and conditional probability estimation (CPE)–based method are proposed, and the failure probability varying with the interval variables can be obtained by only one group Monte Carlo simulation (MCS). Since the computational complexity of CPE is much lower than that of CDE, the CPE-based method is mainly concerned. In the CPE-based method, the conditional failure probability on a realization of the extended interval vector is approximated by that on a differential region adjacent to the corresponding realization; then, the density function estimation required in the CDE can be avoided. In order to ensure the accuracy of the CPE, a strategy is proposed to adaptively select the differential region, in which the MCS can be combined with the CPE (CPE + MCS) and the adaptive Kriging can be nested into the CPE + MCS for improving the efficiency. To improve the efficiency further, the meta-model importance sampling nested Kriging is combined with the CPE-based method. The presented examples illustrate the superiority of the proposed method over the existing methods.

The evaluation of the parameters of multilayered foundations (pavements, runway strips, etc.) plays an important role in ensuring the safe movement of vehicles. An approach of model construction for estimating the mechanical and geometric parameters of such foundations based on the solutions of inverse problems for multilayered elastic packets is proposed. As input data for such problems the measured displacements (or velocities) of certain points on the package surface are used. The proposed approach is based on informational-probabilistic paradigm for inverse problem analysis, whose task is to obtain a posteriori probability density in the space of unknown parameters. The essence of the approach is the block-parametric approximation of the a priori probability density and likelihood function in the spaces of parameters and model data of the problem. The method allows estimating the parameters of the a priori distribution of unknown variable parameters, identifying and excluding outliers of the measured data from the created model, and constructing a posteriori estimation of the unknown parameters’ probability density with acceptable resolution. Proposed method can be used to create a new generation of equipment intended for nondestructive monitoring and estimating of the condition of pavements, runways and foundations of artificial structures. The appropriate software for such high-speed scanning devices that allow on-the-fly display of the diagnosed layered foundation parameters can be developed.

High-speed algorithms for detecting and localizing randomly distributed pulse point objects capable of generating instantaneous delta pulses at random time instances. The search is carried out using the reception device (detector), which can freely move within the search interval and dynamically adjust the size of the scanning window. In this work the a priori information about the distribution of the sought signal source is limited by the single-mode functions with a stepwise probability density function, which provides that the algorithms are physically implementable. The parameters of the optimal search are computed in dependence on the a priori probability density function of the sought signal source and the required localization accuracy.

The paper proposes an approach to development of a mathematical model for determination of monitoring objects using informativeness of their monitoring indicators. The possibility of calculating informativeness of an individual monitoring indicator has been investigated and lines of further research have been determined in terms of calculating the possibility of monitoring source (object) recognition error depending on relative informativeness of the signature and a priori probability of monitoring sources (objects).

This paper analyses the performance of target detection and localization methods in heterogeneous sensor networks using compartmental model, which is an attenuation model expressing the variation of received signal strength (RSS) with propagation distance. First, we compute the threshold for the proposed target detection scheme, based on the decision fusion of different sensors and without requiring a priori probability. We also derive the bound on the threshold and subsequently the lower and upper bounds on the detection and false-alarm probabilities. Next, the location of the detected target is estimated using iterative mini-batch Singular Value Decomposition (SVD) methods in the presence of sensor location uncertainty. We highlight that the method for localization has low computational complexity which is suitable for Internet of Things (IoT) networks. The effectiveness of the compartmental model is demonstrated using both simulation study and real experiments. The model parameters are estimated using WiFi signal strength received on the mobile phones from the access points in an indoor environment.

We consider here a certain class of groupoids obtained via an equivalence relation (the so-called subgroupoids of pair groupoids). We generalize to Haar systems in these groupoids some results related to entropy and pressure which are well known in thermodynamic formalism. We introduce a transfer operator, where the equivalence relation (which defines the groupoid) plays the role of the dynamics and the corresponding transverse function plays the role of the a priori probability. We also introduce the concept of invariant transverse probability and of entropy for an invariant transverse probability, as well as of pressure for transverse functions. Moreover, we explore the relation between quasi-invariant probabilities and transverse measures. Some of the general results presented here are not for continuous modular functions but for the more general class of measurable modular functions.

The secondary search for small objects on the ground by a group of unmanned aerial vehicles (UAVs) is considered. The drone path is planned by optimizing the throughput, an entropy-based characteristic estimated as the volume of useful information acquired per unit time. The proposed approach increases the search efficiency in comparison with planning based on the maximum a priori probability that objects are present.

The planning of drone trajectories in the search for ground-level objects is considered, in the case of challenging observation conditions. In existing planning algorithms, a priori data regarding the location of the objects are used in the secondary search, but no account is taken of their local visibility (on account of fog or smoke, say). To improve search productivity in challenging observation conditions, the observability may be taken into account in planning the drone trajectory. Heuristic models are used to assess the observability. The working trajectory is selected by calculating the maximum useful search information obtained from various possible trajectories. To eliminate Shannon indeterminacy in the utility of the information regarding successive points, we introduce an additional utility function. The results obtained by simulation of the search process confirm that this approach is more effective than trajectory planning on the basis of the maximum a priori probability that objects are present and on the basis of search entropy estimates.

In general, the industrial processes are semi-automatic, and are controlled by the operators. Since the operation principles of the industrial processes are complicated, it is difficult to label observations. The disturbances may be contained in the observations. Therefore, the unsupervised anomaly detection method is promising for research in the industrial processes. In the paper, a multivariate anomaly detection method is proposed, which is unsupervised and online. The priori probability of anomaly occurrence is necessary, and a hazard function selection method is defined at first. Secondly, Bayesian-based method is adopted for anomaly detection. In final, the Dempster-Shafer theory is introduced for fusing the univariate anomaly detection results. The numerical simulation is used for illustrating the anomaly detection power of the proposed method, and the TE process is implemented for testing the fault detection effectiveness. A real data set collected from a bathyscaphe is applied for demonstrating the power of leakage detection.

This paper focuses on the detection of cyber-attacks in a timed probabilistic setting. The plant and the possible attacks are described in terms of a labeled continuous time Markov model that includes both observable and unobservable events, and where each attack corresponds to a particular subset of states. Consequently, attack detection is reformulated as a state estimation problem. A verification methodology is described using a parallel-like composition of the Markov model and its logical observer. The construction of this parallel composition allows us to (i) concisely characterize the set of attacks that can be detected based on the sequences of observations they generate, and (ii) compute performance indicators of interest, such as the a priori probability of an undetectable attack, the average detectability, and the mean delay to detection.

Frame properties and conditions are determined that would minimize the error in signal reconstruction or estimation in the presence of noise and erasures. The special focus here is on stochastic models. These include estimating a random signal with zero mean and a general covariance matrix, minimizing the mean-squared error (MSE) when the frame coefficients are erased according to some a priori probability distribution in the presence of random noise, and also studying the use of stochastic frames in estimating a random signal. In estimating a random signal from noisy coefficients, when a frame coefficient is lost or erased, it is established that the MSE is minimized under certain geometric relationships between the frame vectors and the signal. When the coefficients are erased according to some a priori distribution, conditions are found for the norms of the frame vectors in terms of the probability distribution of the erasure so that the MSE is minimized. Results obtained here also show how using stochastic frames can lead to more flexibility in design and greater control on the MSE.

The paper considers the information resources protection assessing in the information security management in an industrial enterprise. The main aspects of information security as a process are given. It is proposed to use data integrity as a criterion for resources security assessing of the corporate information system, defined as the probability of a possible violation of the integrity in the corresponding process of processing information. The groups of technological operations related to the process of information processing are considered. An approximate set of probabilities of possible events that contribute to maintaining data integrity is given. For the mathematical formulation of the problem, each of the events is considered as an alternative with a given optimization criterion. The introduction of a target function for a variety of alternatives allows you to select the best one and determine the cause of the integrity violation. The dependence of the total probability of integrity violation on a priori probability distribution is noted.

As the most promising technology in wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge in practical implementation because of the high complexity and cost involved in deploying a separate front-end circuit for each antenna. In this paper, we apply the compressive sampling technique to reduce the number of required front-end circuits in the analog domain and the computational complexity in the digital domain. Unlike the commonly adopted random projections, we exploit the a priori probability distribution of the user positions to optimize the compressive sampling strategy, so as to maximize the mutual information between the compressed measurements and the direction-of-arrival (DOA) of user signals. With the optimized compressive sampling strategy, we further propose a compressive sampling Capon spatial spectrum estimator for DOA estimation. In addition, the user signal power is estimated by solving a compressed measurement covariance matrix fitting problem. Furthermore, the user signal waveforms are estimated from a robust adaptive beamformer through the reconstruction of an interference-plus-noise compressed covariance matrix. Simulation results clearly demonstrate the performance advantages of the proposed techniques for user signal parameter estimation as compared to existing techniques.

The article presents and discusses the results of the research regarding the identification of the user groups of financial statements (internal and/or external). Possible misstatements of financial reporting are aimed at developing the ≪desired≫ behavior of these groups. The authors introduced the following theoretical, methodological, instrumental and informational constraints of the carried-out experiment: financial reporting was postulated as an open system of economic measurement; accounting ≪paradoxes≫, formulated and considered by Sokolov Ya.V., were used as the theoretical basis of the a priori probability of existence of inaccurate accounting data in the information space; the formation of learning samples and the general population was carried based on the reporting data on certain business activities. Concurrent fulfillment of these conditions ensured the correct application of the central limit theorem, resulting in a valid statistical conclusion. The ≪paradoxes≫ were divided into complementary pairs with their subsequent formalization through the relevant financial ratios. Actual series of the financial ratios’ distribution were compiled based on the existent populations. The significance of deviation of the obtained series from the normal probability distribution (Gaussian distribution) was estimated. The multimodality of series of the financial ratios distribution was identified and estimated, suggesting the possibility of the existence of intentional, systematic registration errors, and, as a result, presumable manipulation in accounting (financial) reporting. It was found that some economic entities knowingly tamper with financial results, effectively aggravating the ≪paradoxical≫ state. The directions of probable manipulation were revealed, making it possible to define the user groups, whose professional judgment and decisions are affected by the presumed misstatements. The SKRIN database (financial and accounting reporting of 10.0 thousand of food and beverage manufacturers) and government statistics were used as a basis for research; calculations were made using Statistica12 software.

Purpose Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique. Design/methodology/approach The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates. Findings The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully. Originality/value Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

Utilizing Nobel Laureate, Daniel Kahneman's views on decision-making psychology and the general practice experience of Amazon's founder, Jeff Bezos, the authors formulate an approach to understand strategy and entrepreneurship. A hypothetical a priori probability model of a successful entrepreneurial venture is introduced in this paper. The model clarifies why the “prior to launch” probability of success is normally exceptionally low. But, low probability does not imply insignificance as free-market-driven new ventures with low probabilities of success can have a substantial impact on strategic planning within competitive firms. As a venture progresses through start-up phases and the probabilities of achieving individual critical single-events rise, the probability of venture success also increases as a function resembling the shape of a “hockey stick.” Entrepreneurially, the results show how a new venture's start-up can be quantitatively managed and analyzed with improved outcomes. The results provide some strategic insights on the difficulty of assessing competition as markets evolve.

When using the naive Bayesian classifier (NBC), it is necessary to assume that the attributes are independent. Although the relationship between the attributes can be analyzed by the Bayesian network (BN), the network structure and parameters need to be trained. Meanwhile, each continuous attribute is given a priori probability distribution for both the NBC and the BN. If the actual probability distribution of the samples is inconsistent with the priori probability distribution, the classification result is poor. In this paper, a model-free Bayesian classifier (MFBC) is proposed to cope with the drawbacks of the NBC and the BN. The information of the sample space and the joint probability density can be obtained based on the nearest neighbor (NN) strategy. The proposed MFBC is able to cope with discrete or continuous attributes even if there is no a priori probability distribution of the attributes. It is also not necessary to build a network structure to obtain a unitive calculation framework for the attributes. Moreover, the probability distribution for limited amounts of samples is approximated using the NN method. Then, the classification label of a sample can be predicted by the MFBC. The sensitivity analysis of the MFBC is obtained using the UCI data sets. Compared with some classical and ensemble classifiers, the analysis results verify the effectiveness and convergence performance of the MFBC.

A promising response to the threat of external world skepticism involves arguing that our commonsense view of the world best explains the sensory experiences that we have. Since our commonsense view of the world best explains our evidence, we are justified in accepting this commonsense view of the world. Despite the plausibility of this Explanationist Response, it has recently come under attack. James Beebe has argued that only a version of the Explanationist Response that provides an a priori justification of inference to the best explanation can hope to respond to two serious objections. Additionally, he has argued that providing such an a priori justification requires an acceptable account of a priori probability and that it is unclear whether such an account can be developed. In this paper I argue that Beebe fails to provide adequate support for either of these claims.

International audience; In this paper we characterize normal sequences and finite-state dimension in terms of the automatic Kolmogorov complexity and finite-state a priori probability. We show that many known results about normal sequences and finite-state dimension, including the equivalence between aligned and non-aligned normality, Wall's theorem, Piatetski–Shapiro's theorem, Champernowne's example of normal number and its modifications, equivalences between different definitions of finite-state dimension, Agafonov's and Schnorr's results about finite-state selection rules, become easy corollaries of this characterization. For that we use notions of automatic (finite-state) complexity and finite-state a priori probability that are the natural counterparts of the notions of Kolmogorov complexity and Solomonoff–Levin a priori probability in the algorithmic information theory. We also give a machine-independent characterization of normality and finite-state dimension in terms of superadditive calibrated functions. We compare our approach with previous results and notions relating finite automata and complexity.

The inverse problem of electrocardiography (ECG) aims to reconstruct cardiac electrical activity using body surface potential measurements and a mathematical model of the body. However, this problem is ill-posed; therefore, it is essential to use prior information and regularize the solution to get an accurate solution. A statistical estimation has been applied to the inverse ECG problem with success, but a "good" a priori probability model is required. In this study, the Bayesian Maximum A Posteriori (MAP) estimation method is applied for solving the inverse ECG problem. Several prior models (training sets) are constructed, and the corresponding results are evaluated in terms of electrogram reconstruction, activation time estimation and pacing site localization accuracy. Our results showed that the training data consisting of beats from the 1st or 2nd neighbors of the test beat pacing nodes resulted in more successful results, implying that the prior models, including moderate amount and coverage of training data, might lead to an improved reconstruction of electrograms.

When physicians are asked to determine the positive predictive value from the a priori probability of a disease and the sensitivity and false positive rate of a medical test (Bayesian reasoning), it often comes to misjudgments with serious consequences. In daily clinical practice, however, it is not only important that doctors receive a tool with which they can correctly judge—the speed of these judgments is also a crucial factor. In this study, we analyzed accuracy and efficiency in medical Bayesian inferences. In an empirical study we varied information format (probabilities vs. natural frequencies) and visualization (text only vs. tree only) for four contexts. 111 medical students participated in this study by working on four Bayesian tasks with common medical problems. The correctness of their answers was coded and the time spent on task was recorded. The median time for a correct Bayesian inference is fastest in the version with a frequency tree (2:55 min) compared to the version with a probability tree (5:47 min) or to the text only versions based on natural frequencies (4:13 min) or probabilities (9:59 min).The score diagnostic efficiency (calculated by: median time divided by percentage of correct inferences) is best in the version with a frequency tree (4:53 min). Frequency trees allow more accurate and faster judgments. Improving correctness and efficiency in Bayesian tasks might help to decrease overdiagnosis in daily clinical practice, which on the one hand cause cost and on the other hand might endanger patients’ safety.

Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, where portions of the collected data are missing due to sensor instability and communication errors at collection points. These practical issues can be remediated by missing data analysis, which are mainly categorized as either statistical or machine learning(ML)-based approaches. Statistical methods require the prior probability distribution of the data which is unknown in our application. Therefore, we focus on an ML-based approach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes both temporal and spatial characteristics of the data. We evaluate the effectiveness of this approach on a TomTom dataset containing spatio-temporal measurements of average vehicle speed and travel time in the Greater Toronto Area (GTA). We evaluate the method under various conditions, where the results demonstrate that M-RNN outperforms existing solutions,e.g., spline interpolation and matrix completion, by up to 58% decreases in Root Mean Square Error (RMSE)., Comment: presented at the 2020 92nd IEEE conference on vehicular technology, 18 Nov.-16 Dec 2020 6 pages, 5 figures, 2 tables

Trip purpose imputation plays an important role in the development of smart passenger-centered services in Intelligent Transportation Systems. However, previous studies have paid relatively less attention to the trip purpose imputation at an individual level and required no real-time response. To narrow the gaps, a two-phase probabilistic framework TripImputor is proposed in this chapter, to perform real-time taxi trip purpose imputation and personalized recommendation service when passengers get off the taxi. To be more specific, a two-stage clustering algorithm is proposed in the first phase to obtain candidate activity areas (CAAs) in the urban space. Based on that, we extract fine-grained spatiotemporal regularity of human behaviors within each CAA with the Foursquare check-in data. Such process can gain on the priori probability for each kind of human activity and obtain the posterior probabilities of taxi trip purposes by employing Bayes’ theory. In the second phase, historical drop-off locations would be clustered then matched with CAAs for the real-time response. Finally, extensive experiments are conducted to evaluate the effectiveness and response of our proposed methods. The results show that the proposed methods can present accurate trip purposes and offer the recommendation information within 1.6 s averagely.

In today's complex conditions of enterprise operation, innovation processes in most of them are characterized by a set of complex complex organizational measures, which can be implemented only in the implementation of sequentially parallel information-saturated stages of making various management decisions. The article improves and further develops the criterion evaluation of economic decisions on innovation and investment development of the enterprise under conditions of uncertainty and conflict of production and financial and economic processes while taking into account the peculiarities of investment and innovation processes. Based on research papers, the article improves the classification of decision criteria based on the methods of potential theory and the principles of maximum uncertainty functions and inaccuracy functions, which are related to the values of the estimation functional, characteristics of Bayesian sets and Bayesian surfaces.It is proved that for the formation of criteria for certain aspects of ensuring the appropriate level of innovation and investment development of industrial enterprises in modern economic conditions it is advisable to use decision criteria based on methods of obtaining point estimates of the unknown vector of a priori probability distribution in a set. It is proposed to use the Khomenyuk criterion, as well as the Rosenbluth and Herfindahl-Hirschman indices, which are used in determining the indicators of evaluation of the results of economic activity of mining and processing enterprises of Ukraine. The calculations allowed to determine the company with the most stable level of innovation and investment development during the study period. Based on the research, it is concluded that the results of assessing the level of stability of sustainable innovation and investment development of mining and processing enterprises taking into account the risk obtained using the proposed methodological approach can be used for further development of methodology for criterion evaluation of business decisions and conflict in the course of production and financial and economic processes.

According to Huemer (2019), existence is evidence of immortality, provided past time is infinite. The argument is based on, inter alia, an alleged contradiction between the fact of one’s existence now and its improbability. I suggest that Huemer’s argument is flawed in equating the infinitesimally small with its limit value, and in assuming a philosophically significant difference between the a priori probability of the occurrence of a unique incarnation and that of anyone among an infinite number. According to Huemer (2019), existence is evidence of immortality, provided past time is infinite. The argument is based on, inter alia, an alleged contradiction between the fact of one’s existence now and its improbability. I suggest that Huemer’s argument is flawed in equating the infinitesimally small with its limit value, and in assuming a philosophically significant difference between the a priori probability of the occurrence of a unique incarnation and that of anyone among an infinite number.

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks. This work was in part supported by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2020ZT012), Beijing Jiaotong University and China Railway Corporation (Contract No. N2019G028). This article was presented in part at the 2019 IEEE GLOBECOM’19. The associate editor coordinating the review of this article and approving it for publication was O. Oyman. (Corresponding author: Yan Yang.) Yan Yang is with the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

A method for predicting probability characteristics for algorithms of supervised classification of multichannel images is proposed. Implementation of quasi-Bayesian image recognition strategy in conditions of incomplete a priori information about a satellite image is considered. Total weighted probability of correct class recognition is taken as a criterion of effectiveness of decision rule. Weights are interpreted as estimates of a priori probability for occurrence of precedents of the corresponding classes. The procedure for estimating a priori probabilities of classes by Monte Carlo method based on the results of fuzzy classification of samples with fixed size is described; the empirical dependence of the relative error of estimates on the sample size is given.

Inspired by the spatial cognition and localization mechanism of animals, this paper focuses on the organization method of topological nodes for place recognition. The topological map is constructed on the basis of graph by assigning each node with a set of features for localization, in which the inertial/visual odometer provides the required motion parameters. The mapping matrix between nodes and various features is constituted to realize effective organization and utilization of empirical knowledge. The availability criterion of empirical knowledge and the corresponding location error are established based on analysis of the mapping matrix, which provides a priori probability model required in data fusion applications. The topological map of a campus is constructed with scene features and the place recognition result is discussed, which shows the effectiveness of the proposed method.

Underwater glider has an outstanding advantage of long endurance for ocean observation. This paper presents underwater glider path planning with uncertainties under the framework of partially observable Markov decision processes (POMDPs). The kinematics model and the sensor model of the underwater glider have been built, and the uncertainty of action has been taken into consideration. The priori probability distribution and posteriori probability distribution are obtained from the kinematic model and sensor model, respectively. Particle filtering has been used to combine the two probability distributions. Results show that integrating the uncertainties in state estimation can improve the accuracy of waypoint estimation. Obstacle avoidance is also presented in the same framework.

The unmanned underwater vehicle (UUV) plays an ever increasing and important role in the modern marine environment. In particular, the tasks of underwater reconnaissance and surveillance, underwater mine hunting and anti-submarine warfare, all poses a serious and dangerous threat to humans. UUV has become the fore-running technology to accomplish such missions. In this paper, a method based on dynamic Bayesian network modeling was proposed to evaluate the UUV in an underwater threat situation. We divided the UUV threats into three categories: environmental factors, platform factors, and mission factors. Through each of these categories, we carried out factor extraction and set up the priori probability according to the characteristics. Setting up the static Bayesian network involved the addition of state transition probability and establishment of the model for assessing the dynamic Bayesian threat situation. By comparing the results of the static and dynamic Bayesian simulation, it was shown that the dynamic Bayesian is superior. Moreover, by analyzing the sensitivity, we recognized the greatest current threat and in response, determined the appropriate UUV countermeasures. The results showed that the dynamic Bayesian method has great practical significance and value for threat assessment.

Hidden Markov models are a well-known probabilistic graphical model for time series of discrete, partially observable stochastic processes. We consider the method to extend the application of hidden Markov models to non-Gaussian continuous distributions by embedding a priori probability distribution of the state space into reproducing kernel Hilbert space. Corresponding regularization techniques are proposed to reduce the tendency to overfitting and computational complexity of the algorithm, i.e. Nystr¨om subsampling and the general regularization family for inversion of feature and kernel matrices. This method may be applied to various statistical inference and learning problems, including classification, prediction, identification, segmentation, and as an online algorithm it may be used for dynamic data mining and data stream mining. We investigate, both theoretically and empirically, the regularization and approximation bounds of the discrete regularization method. Furthermore, we discuss applications of the method to real-world problems, comparing the approach to several state-of-the-art algorithms.

Travel behavior understanding is a long-standing and critically important topic in the area of smart cities. Big volumes of various GPS-based travel data can be easily collected, among which the taxi GPS trajectory data is a typical example. However, in GPS trajectory data, there is usually little information on travelers’ activities, thereby they can only support limited applications. Quite a few studies have been focused on enriching the semantic meaning for raw data, such as travel mode/purpose inferring. Unfortunately, trip purpose imputation receives relatively less attention and requires no real-time response. To narrow the gap, we propose a probabilistic two-phase framework named TripImputor , for making the real-time taxi trip purpose imputation and recommending services to passengers at their dropoff points. Specifically, in the first phase, we propose a two-stage clustering algorithm to identify candidate activity areas (CAAs) in the urban space. Then, we extract fine-granularity spatial and temporal patterns of human behaviors inside the CAAs from foursquare check-in data to approximate the priori probability for each activity, and compute the posterior probabilities (i.e., infer the trip purposes) using Bayes’ theorem. In the second phase, we take a sophisticated procedure that clusters historical dropoff points and matches the dropoff clusters and CAAs to immerse the real-time response. Finally, we evaluate the effectiveness and efficiency of the proposed two-phase framework using real-world data sets, which consist of road network, check-in data generated by over 38 000 users in one year, and the large-scale taxi trip data generated by over 19 000 taxis in a month in Manhattan, New York City, USA. Experimental results demonstrate that the system is able to infer the trip purpose accurately, and can provide recommendation results to passengers within 1.6 s in Manhattan on average, just using a single normal PC.

As a 3D extension to the High Efficiency Video Coding (HEVC) standard, 3D-HEVC was developed to improve the coding efficiency of multiview videos. It inherits the prediction modes from HEVC, yet both Motion Estimation (ME) and Disparity Estimation (DE) are required for dependent views coding. This improves coding efficiency at the cost of huge computational costs. In this article, an early Merge mode decision approach is proposed for dependent texture views and dependent depth maps coding in 3D-HEVC based on priori and posterior probability models. First, the priori probability model is established by exploiting the hierarchical and interview correlations from those previously encoded blocks. Second, the posterior probability model is built by using the Coded Block Flag (CBF) of the current coding block. Finally, the joint priori and posterior probability model is adopted to early terminate the Merge mode decision for both dependent texture views and dependent depth maps coding. Experimental results show that the proposed approach saves 45.2% and 30.6% encoding time on average for dependent texture views and dependent depth maps coding while maintaining negligible loss of coding efficiency, respectively.

La estadística bayesiana se basa en la probabilidad subjetiva, trabaja con la actualización de la evidencia considerando los conocimientos adquiridos previos a una investigación, más la evidencia obtenida con esta. La interpretación de los resultados requiere la especificación de las hipótesis por contrastar y su probabilidad a priori antes del estudio. La evidencia del estudio se mide con el factor Bayes (razón de la compatibilidad de los datos bajo las hipótesis propuestas). La conjunción de las probabilidades a priori de las hipótesis con el factor Bayes permite calcular la probabilidad a posteriori de cada una. La hipótesis con mayor grado de certidumbre en su actualización es la aceptada para la toma de la decisión. En esta revisión se muestran tres ejemplos de hipótesis por contrastar: diferencia de promedios, correlación y asociación.

Empirical data suggest, that in many situations people are not able to estimate the probability of events on the base of available information in a way consistent with the normative model. This fact infl uences the choice of conclusions which are considered to be rational. The article outlines the factors that may affect the assumed value of a priori probability and – indirectly – the value of a posteriori probability. All these factors will be collectively referred to as the parameter j. Its value depends on the context in which the reasoning is made. In the article I show, that a critical assessment of our reasoning is not always justified.

The distributed intelligent pension system is a new old-age pension system that is designed to solve the problem existed in decentralized management system in traditional nursing homes, such as information isolation and imperfect pension facilities. The system combines the advantages of RFID technology and video linkage monitoring. In order to know whether the elderly is well taken care of, the two types of information need to be processed and analyzed. Data fusion technology is an effective tool to solve the optimal decision of multi attribute data. In the algorithm of data fusion, the neural network algorithm has good fault tolerance and adaptability, and requires a small priori probability distribution of the system. It can handle incomplete and inaccurate information. Combined with the multi-source and massive characteristics of the data of distributed intelligent pension system, the data processing has the characteristics of real-time and accuracy. In addition, the BP neural network has the characteristics of simple realization and high recognition precision in a certain range. The BP neural network algorithm is used as the research, and the additional momentum method is used to improve the traditional BP algorithm. In the same direction, the gradient is added to the weight and threshold, and the algorithm is guaranteed to the direction of convergence.

The problem of joint estimation of parameters and state of continuous time systems using discrete time observations is addressed. The plant parameters are assumed to be modeled by a Wiener process. The a priori probability density function (pdf) of an extended state that comprises the plant state variables and the parameters is propagated in time using an approximate solution of the Fokker-Planck equation that relies on Trotter’s formula for semigroup decomposition. The a posteriori (i. e., given the observations) pdf is then computed at the observation instants using Bayes law.

Canadian Traveler Problem with Neutralizations (CTPN) is a recently introduced challenging graph-theoretic path-planning problem. In CTPN, traversability status of some edges in the underlying graph is dependent on an a priori probability distribution. A traveling agent has two capabilities called disambiguation and neutralization. In the disambiguation case, the true status of an ambiguous edge (traversable or untraversable) is revealed when the agent is at either end of such edges. If the neutralization capability is exploited, the edge immediately becomes traversable. These capabilities are limited and may add a cost of increased path length. The goal of the agent is to find the shortest expected path length by devising an optimal policy that dictates when and where to disambiguate or neutralize. CTPN has important and practical applications within the context of expert and intelligent systems. These include autonomous robot navigation, adaptive transportation systems, naval and land minefield countermeasures, and navigation inside disaster areas for emergency relief operations. There is a recently proposed state-of-the-art exact algorithm that solves CTPN to optimality, called CAON∗ (AO∗ with Caching and Neutralizations). CAON∗ is based on an extension of the well-known AO∗ (AND-OR search) algorithm. Even though CAON∗ has significant improvements compared to its predecessors, it still has exponential run time and space complexity and it has been shown to solve only small instances of CTPN in practice. In this study, we introduce new heuristics for CTPN based on novel strategies that can be used to solve much larger and realistic problem instances. We provide computational experiments on Delaunay graphs to assess and compare the performance of these heuristics and CAON∗, in terms of both run time and solution quality. Our computational experiments indicate that the proposed heuristics run extremely fast (well under a second in all cases) and they result in up to 58% improvement over existing heuristics with respect to expected path lengths with an overall improvement of 32% across our computational experiments.