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To sample from a given target distribution, Markov chain Monte Carlo (MCMC) sampling relies on constructing an ergodic Markov chain with the target distribution as its invariant measure. For any MCMC method, an important question is how to evaluate its efficiency. One approach is to consider the associated empirical measure and how fast it converges to the stationary distribution of the underlying Markov process. Recently, this question has been considered from the perspective of large deviation theory, for different types of MCMC methods, including, e.g., non-reversible Metropolis–Hastings on a finite state space, non-reversible Langevin samplers, the zig-zag sampler, and parallel tempering. This approach, based on large deviations, has proven successful in analysing existing methods and designing new, efficient ones. However, for the Metropolis–Hastings algorithm on more general state spaces, the workhorse of MCMC sampling, the same techniques have not been available for analysing performance, as the underlying Markov chain dynamics violate the conditions used to prove existing large deviation results for empirical measures of a Markov chain. This also extends to methods built on the same idea as Metropolis–Hastings, such as the Metropolis-Adjusted Langevin Method or ABC-MCMC. In this paper, we take the first steps towards such a large-deviations based analysis of Metropolis–Hastings-like methods, by proving a large deviation principle for the empirical measures of Metropolis–Hastings chains. In addition, we also characterize the rate function and its properties in terms of the acceptance- and rejection-part of the Metropolis–Hastings dynamics., Not duplicate with DiVA 1789332QC 20240201

“This is an Accepted Manuscript of an article published by Taylor & Francis in Inverse Problems in Science and Engineering on 26-jun-2019, available online: https://www.tandfonline.com/doi/full/10.1080/17415977.2019.1632841.”, In this paper we introduce a method to estimate a pressure dependent thermal conductivity coefficient arising in a heat diffusion model with applications in food technology. The strong smoothing effect of the corresponding direct problem renders the inverse problem under consideration severely ill-posed. Thus specially tailored methods are necessary in order to obtain a stable solution. Consequently, we model the uncertainty of the conductivity coefficient as a hierarchical Gaussian Markov random field (GMRF) restricted to uniqueness conditions for the solution of the inverse problem established in Fraguela et al. [1]. Furthermore, we propose a Single Variable Exchange Metropolis-Hastings algorithm (SVEMH) to sample the corresponding conditional probability distribution of the conductivity coefficient given observations of the temperature. Sensitivity analysis of the direct problem suggests that large integration times are necessary to identify the thermal conductivity coefficient. Numerical evidence indicates that a signal to noise ratio of roughly 10 cubed suffices to reliably retrieve the thermal conductivity coefficient., Ministerio de Economía y Competitividad (MINECO), Universidad Complutense de Madrid, Depto. de Análisis Matemático y Matemática Aplicada, Fac. de Ciencias Matemáticas, TRUE, pub

This paper addresses the problem of Inductive Synthesis by analysing the Metropolis-Hastings stochastic search algorithm. The goal of Inductive Synthesis is to generate programs whose intended behaviour is established through the use of input and output examples. The Metropolis-Hastings algorithm searches the set of all possible programs and finds possible solutions. Our experiments show that while optimization can be done under certain conditions, it does not improve the algorithm’s success rate in synthesizing programs on complex domains compared to more randomized but domainspecific approaches., CSE3000 Research Project, Computer Science and Engineering

This thesis establishes methods to quantify and explain uncertainty through high-order moments in time series data, along with first principal-based improvements on the standard autoencoder and variational autoencoder. While the first-principal improvements on the standard variational autoencoder provide additional means of explainability, we ultimately look to non-variational methods for quantifying uncertainty under the autoencoder framework. We utilize Shannon's differential entropy to accomplish the task of uncertainty quantification in a general nonlinear and non-Gaussian setting. Together with previously established connections between autoencoders and principal component analysis, we motivate the focus on differential entropy as a proper abstraction of principal component analysis to this more general framework, where nonlinear and non-Gaussian characteristics in the data are permitted. Furthermore, we are able to establish explicit connections between high-order moments in the data to those in the latent space, which induce a natural latent space decomposition, and by extension, an explanation of the estimated uncertainty. The proposed methods are intended to be utilized in economic and financial factor models in state space form, building on recent developments in the application of neural networks to factor models with applications to financial and economic time series analysis. Finally, we demonstrate the efficacy of the proposed methods on high frequency hourly foreign exchange rates, macroeconomic signals, and synthetically generated autoregressive data sets.

Time management and distance evaluation have long been a difficult task for workers and companies. This thesis studies 6712 pothole coordinates in Gävle municipality, and evaluates the minimal total road distance needed to visit each pothole once, and return to an initial pothole. Road distance is approximated using the flight distance and a simple random sample of 113 road distances from Google Maps. Thereafter, the data from the sample along with a Bayesian approach is used to find a distribution of the ratio between road distance and flight distance. Lastly, a solution to the shortest distance is devised using the Nearest Neighbor algorithm (NNA) and Simulated Annealing (SA). Computational work is performed with Markov Chain Monte Carlo (MCMC). The results provide a minimal road distance of 717 km., Tidshantering och distansutvärdering är som regel en svår uppgift för arbetare och företag. Den här uppsatsen studerar 6712 potthål i Gävle kommun, och utvärderar den bilväg som på kortast sträcka besöker varje potthål och återgår till den ursprungliga startpunkten. Bilvägsavståndet mellan potthålen uppskattas med hjälp av flygavståndet, där ett obundet slumpmässigt urval av 113 bilvägsavstånd mellan potthålens koordinatpunkter dras. Bilvägsdistanser hittas med hjälp av Google Maps. Därefter används data från urvalet tillsammans med en bayesiansk modell för att hitta en fördelning för förhållandet mellan bilvägsavstånd och flygavstånd. Slutligen framförs en lösning på det kortaste bilvägsavståndet med hjälp av en Nearest Neighbour algoritm (NNA) samt Simulated Annealing (SA). Statistiskt beräkningsarbete utförs med Markov Chain Monte Carlo (MCMC). Resultaten ger en kortaste bilvägssträcka på 717 km.

U.S. Navy aircraft are required to meet minimum operational availability targets, while minimizing spare parts procurement costs. The current optimization model written by Salmeron and Buss, uses marginal analysis, as described by Sherbrooke, to determine optimal sparing policies for this highly complex multi-indenture model. The literature lacks alternative optimization methodologies for such a problem, so we propose an alternative approach utilizing simulated annealing (SA), a Markov Chain Monte Carlo algorithm. We present three SA approaches tested in three case studies of varying size and complexity. Our initial findings show that in very simple problems, SA is easily capable of outperforming marginal analysis; however, problems with more complexity have large optimality gaps. This is likely because the SA Markov chain is unable to effectively explore the multi-indenture structure of the problem. We implement a method to account for this structure that intelligently builds initial feasible solutions using an epsilon-greedy approach to marginal analysis. This approach produces better results than NAVARM in more than half of the trials on problems of moderate complexity. We also implement a novel method for calculating operational availability that may allow full scale problems to be optimized more efficiently., NAVSUP WSS, Lieutenant Commander, United States Navy, Approved for public release. Distribution is unlimited.

Kuyruk modelleme yöntemi, yüksek emniyete sahip mekanik sistemlerin güvenilirliklerini tahmin etmek için kullanılan etkili bir yöntemdir. Klasik kuyruk modelleme yöntemi, uygun eşik değer belirlendikten sonra ilgilenilen sınır durum fonksiyonuna ait kümülatif dağılım fonksiyonunun bilinen bir dağılıma (örneğin; Genelleştirilmiş Pareto Dağılımı) benzeştirilerek, hasar olasılığı ve güvenilirlik indisi hesabı için bu dağılımın parametrelerinin kullanılmasına dayanır. Klasik kuyruk modelleme yöntemi ile güvenilirlik hesabında, sadece kuyruk bölgesine ait sınır durum fonksiyonu değerleri kullanıldığı için diğer sınır durum fonksiyonu hesaplamaları çoğunlukla boşa gitmektedir. Bu çalışma kapsamında, klasik kuyruk modelleme yönteminin bu dezavantajının üstesinden gelebilmek için yeni bir yöntem geliştirilmiştir. Bu yöntem, Metropolis-Hastings algoritması ile uygulanan Markov Zinciri Monte Carlo tabanlı kuyruk modelleme yöntemidir. Geliştirilen Markov Zinciri Monte Carlo tabanlı kuyruk modelleme yöntemi sadece kuyruk bölgesinden örnekleme yaparak daha etkin güvenilirlik tahminlerinin yapılabilmesine olanak sağlamıştır. Ayrıca, Markov Zinciri Monte Carlo yönteminde kullanılan teklif dağılım denklemine ölçek parametresi eklenmiş ve bu ölçek parametresinin ikinci derece polinom yanıt yüzeyler kullanılarak çeşitli rassal değişken sayılarına sahip örnek problemler için optimum değeri elde edilmiştir. Optimum değer ile problemlerin rassal değişken sayıları arasında yaklaşık bir ilişki kurulmuştur. Sonrasında yatay eksenli bir rüzgâr türbini ve otomobil tork kolu problemleri için güvenilirlik tahmini yapılmış ve ölçek parametresi ile rassal değişken sayıları arasında kurulan ilişki bu problemde denenerek iyi sonuç verdiği görülmüştür. Klasik kuyruk modelleme yöntemi ve projede önerilen yöntem matematik ve mühendislik örnek problemlerine uygulandığında, problemlerin çoğunda önerilen yöntemin doğruluğunun klasik kuyruk modelleme yönteminin doğruluğundan daha yüksek olduğu gözlenmişt

This thesis attempts to evaluate the Markov Chain Monte Carlo (MCMC) methods Metropolis-Hastings (MH) and No-U-Turn Sampler (NUTS) to estimate systemic risk measures. The subject of analysis is an equity portfolio provided by a Nordic asset management firm, which is modelled using a vine copula. The evaluation considers three different crisis outcomes on a portfolio level, and the results are compared with a Monte Carlo (MC) benchmark. The MCMC samplers attempt to increase sampling efficiency by sampling from these crisis events directly, which is impossible for an MC sampler. The resulting systemic risk measures are evaluated both on the portfolio level as well as marginal level. The results are divided. In part, the MCMC samplers proved to be efficient in terms of accepted samples, where NUTS outperformed MH. However, due to the practical implementation of the MCMC samplers and the vine copula model, the computational time required outweighed the gains in sampler efficiency - causing the MC sampler to outperform both MCMC samplers in certain settings. For NUTS, there seems to be great potential in the context of estimating systemic risk measures as it explores high-dimensional and multimodal joint distributions efficiently with low autocorrelation. It is concluded that asset management companies can benefit from both using vine copulas to model portfolio risk, as well as using MC or MCMC methods for evaluating systemic risk. However, for the MCMC samplers to be of practical relevance, it is recommended to further investigate efficient implementations of vine copulas in the context of MCMC sampling., Detta examensarbete utvärderar Markov Chain Monte Carlo (MCMC)-metoderna No-U-Turn Sampler (NUTS) och Metropolis-Hastings (MH) vid uppskattning av systemiska riskmått. För att göra detta används en vine copula för att modellera en portfölj, baserad på empirisk data från ett nordiskt kapitalförvaltningsföretag. Metoderna utvärderas givet tre olika krishändelser och jämförs därefter med ett Monte Carlo (MC) benchmark. MCMC-metoderna försöker öka samplingseffektiviteten genom att simulera direkt från dessa krishändelser, vilket är omöjligt för en klassisk MC-metod. De resulterande systemiska riskmåtten utvärderas både på portföljnivå och på marginalnivå. Resultaten är delade. Dels visade sig MCMC-metoderna vara effektiva när det gäller accepterade samples där NUTS överträffade MH. Dock, med anledning av av den praktiska implementationen av MCMC-metoderna och vine copula modellen var beräkningstiden för hög trots effektiviteten hos metoden - vilket fick MC-metoden att överträffa de andra metoderna i givet dessa särskilda kontexter. När det kommer till att uppskatta systemiska riskmått finns det dock stor potential för NUTS eftersom metoden utforskar högdimensionella och multimodala sannolikhetsfördelningar effektivt med låg autokorrelation. Vi drar även slutsatsen att kapitalförvaltare kan dra nytta av att både använda riskmodeller baserade på vine copulas, samt använda MC- eller MCMC-metoder för att utvärdera systemisk risk. För att MCMC-metoderna ska vara av praktisk relevans rekommenderas det dock att framtida forskning görs där mer effektiva implementeringar av vine copula-baserade modeller görs i samband med MCMC-sampling.

Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.

Circular data are encountered throughout a variety of scientific disciplines, such as in eye movement research as the direction of saccades. Motivated by such applications, mixtures of peaked circular distributions are developed. The peaked distributions are a novel family of Batschelet-type distributions, where the shape of the distribution is warped by means of a transformation function. Because the Inverse Batschelet distribution features an implicit inverse that is not computationally feasible for large or complex data, an alternative called the Power Batschelet distribution is introduced. This distribution is easy to compute and mimics the behavior of the Inverse Batschelet distribution. Inference is performed in both the frequentist framework, through Expectation–Maximization (EM) and the bootstrap, and the Bayesian framework, through MCMC. All parameters can be fixed, which may be done by assumption to reduce the number of parameters. Model comparison can be performed through information criteria or through bridge sampling in the Bayesian framework, which allows performing a wealth of hypothesis tests through the Bayes factor. An R package, flexcircmix, is available to perform these analyses.

This study contributes to the field of cycling route choice by adopting the unprecedented combination of the Metropolis-Hastings (MH) path-sampling algorithm and the Expanded Path Size Logit (EPSL) model. The MH sampling approach is used to generate 15 alternative route choice sets for cyclists. The EPSL multivariate route choice framework is utilized to account for the correlation between sampled and non-sampled alternatives (joint MH-EPSL model). The data used in this paper is drawn from GPS data collected by the City of Toronto using a custom-built smartphone application in 2014–2015. The study focuses on non-work-related cycling trips (shopping, leisure, social and others) in downtown Toronto on weekdays. The estimated results indicate that the presence of bicycle lanes and road medians attractions and number of trees along the path have a positive impact on cyclist route choice. In general, cyclists prefer to take shorter routes on lower speed roads with less public transit stops especially during the evening rush hour, and less willing to take one-way streets, local roads, and steep road segments. These findings are useful to policy makers as well as transportation and urban designers when developing a cycling network aiming to attract more cyclists. Finally, our results indicate that the MH-EPSL model performance is an appropriate framework to study cyclists’ route choice decisions.

Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models. There is no closed-form solution available for this problem, implying that we are forced to use approximations. In this tutorial we will provide a self-contained introduction to one of the state-of-the-art methods the particle Metropolis-Hastings algorithm which has proven to offer a practical approximation. This is a Monte Carlo based method, where the particle filter is used to guide a Markov chain Monte Carlo method through the parameter space. One of the key merits of the particle Metropolis-Hastings algorithm is that it is guaranteed to converge to the "true solution" under mild assumptions, despite being based on a particle filter with only a finite number of particles. We will also provide a motivating numerical example illustrating the method using a modeling language tailored for sequential Monte Carlo methods. The intention of modeling languages of this kind is to open up the power of sophisticated Monte Carlo methods including particle Metropolis-Hastings to a large group of users without requiring them to know all the underlying mathematical details.

Background: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical difficulties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters. Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines. Conclusions: VMCMC is a free software available under the New BSD License. Executable jar files, tutorial manual and source code can be downloaded from https://bitbucket.org/rhali/visualmcmc/., QC 20170607

We present a general framework for Bayesian estimation of incompletely observed multivariate diffusion processes. Observations are assumed to be discrete in time, noisy and incomplete. We assume the drift and diffusion coefficient depend on an unknown parameter. A data-augmentation algorithm for drawing from the posterior distribution is presented which is based on simulating diffusion bridges conditional on a noisy incomplete observation at an intermediate time. The dynamics of such filtered bridges are derived and it is shown how these can be simulated using a generalised version of the guided proposals introduced in Schauer, Van der Meulen and Van Zanten (2017, Bernoulli 23(4A))., Statistics

Dirichlet process mixture (DPM) has been widely used as exible prior in nonparametric Bayesian literature, and Weighted Dirichlet process mixture (WDPM) can be viewed as extension of DPM which relaxes model distribution assumptions. Meanwhile, WDPM requires to set weight functions and can cause extra computation burden. In this dissertation, we develop more efficient and exible WDPM approaches under three research topics. The first one is semiparametric cubic spline regression where we adopt a nonparametric prior for error terms in order to automatically handle heterogeneity of measurement errors or unknown mixture distribution, the second one is to provide an innovative way to construct weight function and illustrate some decent properties and computation efficiency of this weight under semiparametric stochastic volatility (SV) model, and the last one is to develop WDPM approach for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model (as an alternative approach for SV model) and propose a new model evaluation approach for GARCH which produces easier-to-interpret result compared to the canonical marginal likelihood approach. In the first topic, the response variable is modeled as the sum of three parts. One part is a linear function of covariates that enter the model parametrically. The second part is an additive nonparametric model. The covariates whose relationships to response variable are unclear will be included in the model nonparametrically using Lancaster and Šalkauskas bases. The third part is error terms whose means and variance are assumed to follow non-parametric priors. Therefore we denote our model as dual-semiparametric regression because we include nonparametric idea for both modeling mean part and error terms. Instead of assuming all of the error terms follow the same prior in DPM, our WDPM provides multiple candidate priors for each observation to select with certain probability. Such probability (or weight) is modeled by releva

We consider the previously unsolved problem of sampling paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and hence also forbids to compute the normalizing constant of the sampling distribution. The problem is important because the ability to sample from a known distribution introduces mathematical rigor into many applications, including the estimation of choice models with sampling of alternatives that can be formalized as paths in a decision network (most obviously route choice), probabilistic map matching, dynamic traffic assignment, and route guidance., QC 20130213

We present a stochastic model and numerical simulation framework for a synthetic nanoscale walker that can be used to transport materials and information at superdiffusive rates in artificial molecular systems. Our \emph{multivalent random walker} model describes the motion of a walker with a rigid, inert body and flexible, enzymatic legs. A leg can bind to and irreversibly modify surface-bound chemical substrate sites arranged as nanoscale tracks. As the legs attach to, modify, and detach from the sites, the walker moves along these tracks. Walkers are symmetrical and the tracks they walk on are unoriented, yet we show that under appropriate kinetic constraints the walkers can transform the chemical free energy in the surface sites into directional motion, and can do ordered work against an external load force. This shows that multivalent random walkers are a new type of molecular motor, useful for directional transport in nanoscale systems. We model the motion of multivalent random walkers as a continuous-time discrete-state Markov process. States in the process correspond to the chemical state of the legs and surface sites, and transitions represent discrete chemical changes of legs binding to, unbinding from, and modifying the surface sites. The Markov property holds because we let the mechanical motion of the body and unattached legs come to equilibrium in between successive chemical steps, thus the transitions depend only on the current chemical state of the surface sites and attached legs. This coarse-grained model of walker motion allows us to use both equilibrium and non-equilibrium Markov chain Monte Carlo simulation techniques. The Metropolis-Hastings algorithm approximates the motion of a walker's body and legs at a mechanical equilibrium, while the kinetic Monte Carlo algorithm simulates the transient chemical dynamics of the walker stepping across the surface sites. Using these numerical techniques, we find that MVRWs move superdiffusively in the direc

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also rapidly sample uncorrelated configurations. Here, we introduce a new class of moves based on nonequilibrium dynamics: candidate configurations are generated through a finite-time process in which a system is actively driven out of equilibrium, and accepted with criteria that preserve the equilibrium distribution. The acceptance rule is similar to the Metropolis acceptance probability, but related to the nonequilibrium work rather than the instantaneous energy difference. Our method is applicable to sampling from both a single thermodynamic state or a mixture of thermodynamic states, and allows both coordinates and thermodynamic parameters to be driven in nonequilibrium proposals. While generating finite-time switching trajectories incurs an additional cost, driving some degrees of freedom while allowing others to evolve naturally can lead to large enhancements in acceptance probabilities, greatly reducing structural correlation times. Using nonequilibrium driven processes vastly expands the repertoire of useful Monte Carlo proposals in simulations of dense solvated systems.

We describe a new approach to the sampling of route choice sets using the Metropolis-Hastingsalgorithm., TSC import 993 2012-02-06. QC 20120425

Relational learning deals with the setting where one has multiple sources of data, each describing different properties of the same set of entities. We are concerned primarily with settings where the properties are pairwise relations between entities, and attributes of entities. We want to predict the value of relations and attributes, but relations between entities violate the basic statistical assumption of exchangeable data points, or entities. Furthermore, we desire models that scale gracefully as the number of entities and relations increase. This thesis rests on two claims, that (i) that Collective Matrix Factorization can effectively integrate different sources of data to improve prediction; and, (ii) that training scales well as the number of entities and observations increase. We consider two real-world data sets in experimental support of these claims: augmented collaborative filtering and augmented brain imaging. In augmented collaborative filtering, we show that genre information about movies can be used to increase the predictive accuracy of user's ratings. In augmented brain imaging, we show that word co-occurrence information can be used to increase the predictive accuracy of a model of changes in brain activity to word stimuli, even in regions of the brain that were never included in the training data., Sponsored in part by NSF grant nos. SBE0354420 and ACI012671.

This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose that the researcher wishes to estimate the effects of covariates on the binary responses, whilst taking into account underlying spatial variation, which is itself of some interest. This situation arises in many contexts and the dingo, cypress and toad case studies described in the motivation chapter are examples of this. Two main approaches to modelling and inference are investigated in this thesis. The first is frequentist and based on generalized linear models, with spatial variation modelled by using a block structure or by smoothing the residuals spatially. The EM algorithm can be used to obtain point estimates, coupled with bootstrapping or asymptotic MLE estimates for standard errors. The second approach is Bayesian and based on a three- or four-tier hierarchical model, comprising a logistic regression with covariates for the data layer, a binary Markov Random field (MRF) for the underlying spatial process, and suitable priors for parameters in these main models. The three-parameter autologistic model is a particular MRF of interest. Markov chain Monte Carlo (MCMC) methods comprising hybrid Metropolis/Gibbs samplers is suitable for computation in this situation. Model performance can be gauged by MCMC diagnostics. Model choice can be assessed by incorporating another tier in the modelling hierarchy. This requires evaluation of a normalization constant, a notoriously difficult prob

Hlavným cieľom tejto práce bolo navrhnúť a implementovať nástroj, ktorý by bol schopný klasifikovať sekvencie génu 16S rRNA do taxonomických kategórií s využitím vlastností génu 16S rRNA. Vytvorený nástroj analyzuje všetky vstupné sekvencie súčasne, čím sa líši od bežných klasifikačných prístupov, ktoré klasifikujú vstupné sekvencie jednotlivo. Tento nástroj využíva znalosť, že baktérie obsahujú niekoľko kópií génu 16S rRNA, ktoré sa môžu svojou sekvenciou odlišovať. Jedným z hlavných prínosov tejto práce je práve návrh, implementácia a vyhodnotenie schopností tohto nástroja. Experimenty ukázali, že navrhnutý nástroj je pre menšie dátové sady schopný identifikovať odpovedajúce baktérie a určiť správne pomery ich abundancií. Pri väčších dátových sadách sa však prehľadávaný priestor stáva veľmi rozsiahly a členitý, čo vyžaduje ďalšie vylepšenia navrhovaného nástroja, aby bol stavový priestor schopný prehľadávať efektívne., The main goal of this thesis was to design and implement a tool that would be able to classify the sequences of the 16S rRNA gene into taxonomic categories using the properties of the 16S rRNA gene. The created tool analyzes all input sequences simultaneously, which differs from common classification approaches, which classify input sequences individually. This tool relies on the fact that bacteria contain several copies of the 16S rRNA gene, which may differ in sequence. The main contribution of this work is design, implementation and evaluation of the capabilities of this tool. Experiments have shown that the proposed tool is able to identify the corresponding bacteria for smaller datasets and determine the correct ratios of their abundances. However, with larger datasets, the state space becomes very large and fragmented, which requires further improvements in order for it to search the state space in an efficient way.

Hlavným cieľom tejto práce bolo navrhnúť a implementovať nástroj, ktorý by bol schopný klasifikovať sekvencie génu 16S rRNA do taxonomických kategórií s využitím vlastností génu 16S rRNA. Vytvorený nástroj analyzuje všetky vstupné sekvencie súčasne, čím sa líši od bežných klasifikačných prístupov, ktoré klasifikujú vstupné sekvencie jednotlivo. Tento nástroj využíva znalosť, že baktérie obsahujú niekoľko kópií génu 16S rRNA, ktoré sa môžu svojou sekvenciou odlišovať. Jedným z hlavných prínosov tejto práce je práve návrh, implementácia a vyhodnotenie schopností tohto nástroja. Experimenty ukázali, že navrhnutý nástroj je pre menšie dátové sady schopný identifikovať odpovedajúce baktérie a určiť správne pomery ich abundancií. Pri väčších dátových sadách sa však prehľadávaný priestor stáva veľmi rozsiahly a členitý, čo vyžaduje ďalšie vylepšenia navrhovaného nástroja, aby bol stavový priestor schopný prehľadávať efektívne., The main goal of this thesis was to design and implement a tool that would be able to classify the sequences of the 16S rRNA gene into taxonomic categories using the properties of the 16S rRNA gene. The created tool analyzes all input sequences simultaneously, which differs from common classification approaches, which classify input sequences individually. This tool relies on the fact that bacteria contain several copies of the 16S rRNA gene, which may differ in sequence. The main contribution of this work is design, implementation and evaluation of the capabilities of this tool. Experiments have shown that the proposed tool is able to identify the corresponding bacteria for smaller datasets and determine the correct ratios of their abundances. However, with larger datasets, the state space becomes very large and fragmented, which requires further improvements in order for it to search the state space in an efficient way.

Hlavným cieľom tejto práce bolo navrhnúť a implementovať nástroj, ktorý by bol schopný klasifikovať sekvencie génu 16S rRNA do taxonomických kategórií s využitím vlastností génu 16S rRNA. Vytvorený nástroj analyzuje všetky vstupné sekvencie súčasne, čím sa líši od bežných klasifikačných prístupov, ktoré klasifikujú vstupné sekvencie jednotlivo. Tento nástroj využíva znalosť, že baktérie obsahujú niekoľko kópií génu 16S rRNA, ktoré sa môžu svojou sekvenciou odlišovať. Jedným z hlavných prínosov tejto práce je práve návrh, implementácia a vyhodnotenie schopností tohto nástroja. Experimenty ukázali, že navrhnutý nástroj je pre menšie dátové sady schopný identifikovať odpovedajúce baktérie a určiť správne pomery ich abundancií. Pri väčších dátových sadách sa však prehľadávaný priestor stáva veľmi rozsiahly a členitý, čo vyžaduje ďalšie vylepšenia navrhovaného nástroja, aby bol stavový priestor schopný prehľadávať efektívne., The main goal of this thesis was to design and implement a tool that would be able to classify the sequences of the 16S rRNA gene into taxonomic categories using the properties of the 16S rRNA gene. The created tool analyzes all input sequences simultaneously, which differs from common classification approaches, which classify input sequences individually. This tool relies on the fact that bacteria contain several copies of the 16S rRNA gene, which may differ in sequence. The main contribution of this work is design, implementation and evaluation of the capabilities of this tool. Experiments have shown that the proposed tool is able to identify the corresponding bacteria for smaller datasets and determine the correct ratios of their abundances. However, with larger datasets, the state space becomes very large and fragmented, which requires further improvements in order for it to search the state space in an efficient way.

Hlavným cieľom tejto práce bolo navrhnúť a implementovať nástroj, ktorý by bol schopný klasifikovať sekvencie génu 16S rRNA do taxonomických kategórií s využitím vlastností génu 16S rRNA. Vytvorený nástroj analyzuje všetky vstupné sekvencie súčasne, čím sa líši od bežných klasifikačných prístupov, ktoré klasifikujú vstupné sekvencie jednotlivo. Tento nástroj využíva znalosť, že baktérie obsahujú niekoľko kópií génu 16S rRNA, ktoré sa môžu svojou sekvenciou odlišovať. Jedným z hlavných prínosov tejto práce je práve návrh, implementácia a vyhodnotenie schopností tohto nástroja. Experimenty ukázali, že navrhnutý nástroj je pre menšie dátové sady schopný identifikovať odpovedajúce baktérie a určiť správne pomery ich abundancií. Pri väčších dátových sadách sa však prehľadávaný priestor stáva veľmi rozsiahly a členitý, čo vyžaduje ďalšie vylepšenia navrhovaného nástroja, aby bol stavový priestor schopný prehľadávať efektívne., The main goal of this thesis was to design and implement a tool that would be able to classify the sequences of the 16S rRNA gene into taxonomic categories using the properties of the 16S rRNA gene. The created tool analyzes all input sequences simultaneously, which differs from common classification approaches, which classify input sequences individually. This tool relies on the fact that bacteria contain several copies of the 16S rRNA gene, which may differ in sequence. The main contribution of this work is design, implementation and evaluation of the capabilities of this tool. Experiments have shown that the proposed tool is able to identify the corresponding bacteria for smaller datasets and determine the correct ratios of their abundances. However, with larger datasets, the state space becomes very large and fragmented, which requires further improvements in order for it to search the state space in an efficient way.

Hlavným cieľom tejto práce bolo navrhnúť a implementovať nástroj, ktorý by bol schopný klasifikovať sekvencie génu 16S rRNA do taxonomických kategórií s využitím vlastností génu 16S rRNA. Vytvorený nástroj analyzuje všetky vstupné sekvencie súčasne, čím sa líši od bežných klasifikačných prístupov, ktoré klasifikujú vstupné sekvencie jednotlivo. Tento nástroj využíva znalosť, že baktérie obsahujú niekoľko kópií génu 16S rRNA, ktoré sa môžu svojou sekvenciou odlišovať. Jedným z hlavných prínosov tejto práce je práve návrh, implementácia a vyhodnotenie schopností tohto nástroja. Experimenty ukázali, že navrhnutý nástroj je pre menšie dátové sady schopný identifikovať odpovedajúce baktérie a určiť správne pomery ich abundancií. Pri väčších dátových sadách sa však prehľadávaný priestor stáva veľmi rozsiahly a členitý, čo vyžaduje ďalšie vylepšenia navrhovaného nástroja, aby bol stavový priestor schopný prehľadávať efektívne., The main goal of this thesis was to design and implement a tool that would be able to classify the sequences of the 16S rRNA gene into taxonomic categories using the properties of the 16S rRNA gene. The created tool analyzes all input sequences simultaneously, which differs from common classification approaches, which classify input sequences individually. This tool relies on the fact that bacteria contain several copies of the 16S rRNA gene, which may differ in sequence. The main contribution of this work is design, implementation and evaluation of the capabilities of this tool. Experiments have shown that the proposed tool is able to identify the corresponding bacteria for smaller datasets and determine the correct ratios of their abundances. However, with larger datasets, the state space becomes very large and fragmented, which requires further improvements in order for it to search the state space in an efficient way.