bayesian reasoning research papers


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Bayesian reasoning in science
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Ours is an uncertain world, though fortunately all things are not equally uncertain. But we are used to grading uncertainty. We are fairly certain, for example, that the textbook laws of physics will remain valid for the foreseeable future, and very certain that they will remain in

Bayesian reasoning in an abductive mechanism for argument generation and analysis
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Abstract Our argumentation system, NAG, uses Bayesian etworks in a user model and in a normative model to assemble and assess arguments which balance persuasiveness with normative correctness. Attentional focus is simulated in both models to select relevant

Frequency interpretation of ambiguous statistical information facilitates Bayesian reasoning
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Abstract The idea that naturally sampled frequencies facilitate performance in statistical reasoning tasks because they are a cognitively privileged representational format has been challenged by findings that similarly structured numbers presented as chances similarly

Bayesian reasoning in managerial decisions on the choice of equipment for the prevention of industrial accidents
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The managerial problem of the choice among alternative protective equipment used to prevent industrial (technological) accidents is considered. The choice takes into account potential failures of the equipment or, conversely, the equipment reliability. Such failures

ASSESSING PSYCHOLOGY STUDENTS DIFFICULTIES WITH CONDITIONAL PROBABILITY AND BAYESIAN REASONING
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METHODS The building of the questionnaire was based in a rigorous methodological process, including expert judgment, item trialling, validity and reliability assessment and Bayesian estimation of items psychometric features. All the students in the different

Numeracy, frequency, and Bayesian reasoning
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ABSTRACT Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single- event probabilities. A questionnaire study of 342 college students replicated this effect but

A neural implementation of conceptual hierarchies with Bayesian reasoning
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Abstract We present a scheme for translating high-level descriptions of conceptual hierarchies into a neural network representation. The intuitive semantics of a conceptual hierarchy is provided by a Bayesian net, and the neural network implementation provably

Theory-based Bayesian models of inductive learning and reasoning
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Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of

Semantic sensitive region retrieval using keyword-integrated bayesian reasoning
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Abstract. Inaccurate semantic representation is the main cause of semantic gaps in content- based image retrieval, owing to: 1. imprecise semantic description at image global scale; 2. inaccurate mapping from visual features to image perception. In this paper, we propose a

A Bayesian analysis of some forms of inductive reasoning
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and category-level information enables a rich set of predictions. For example, you might not be able to predict much about Peter until

A comparison of Bayesian and belief function reasoning
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The goal of this paper is to compare the similarities and differences between Bayesian and belief function reasoning. Our main conclusion is that although there are obvious differences in semantics, representations, and the rules for combining and marginalizing

Bart: A Bayesian reasoning tool for knowledge based systems
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ABSTRACT As the technology for building knowledge based systems has matured, important lessons have been learned about the relationship between the architecture of a system and the nature of the problems it is intended to solve. We are implementing a knowledge

When to Teach Bayesian Reasoning
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To the Edtfor;―Gigerenzer s articlel on how changes in problem format affect the difficulty physicians have us- ing a Bayesian reasoning strategy was fascinating, espe- cially from the perspective of how to teach decision-mak- ing principles to medical students. Typically,

Hierarchical bayesian networks: A probabilistic reasoning model for structured domains
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Abstract Bayesian Networks are being used extensively for reasoning under uncertainty. Inference mechanisms for Bayesian Networks are compromised by the fact that they can only deal with propositional domains. In this work, we introduce an extension of that

Tractable reasoning with Bayesian description logics
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The DL-Lite family of tractable description logics lies between the semantic web languages RDFS and OWL Lite. In this paper, we present a probabilistic generalization of the DL-Lite description logics, which is based on Bayesian networks. As an important feature, the new

Distributed multi-agent probabilistic reasoning with bayesian networks
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Main stream approaches in distributed artificial intelligence (DAI) are essentially logic- based. Little has been reported to explore probabilistic approach in DAI. On the other hand, Bayesian networks have been applied to many AI tasks that require reasoning under

Bayesian reasoning for Laban Movement Analysis used in human-machine interaction
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We present the implementation of computational Laban Movement Analysis (LMA) for human-machine interaction using Bayesian reasoning. The research field of computational human movement analysis is lacking a general underlying modelling language, ie, how to

Reasoning under uncertainty in natural language dialogue using Bayesian networks
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In which uncertainty in natural language dialogue is introduced as the central problem in the research described in this thesis. The idea of using of Bayesian networks is hypothesised as a possible solution to this problem. Dialogue acts are presented as the central notion in

Bayesian networks and decision-theoretic reasoning for artificial intelligence
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For her science project, which she called Dr. Sigmund Microchip, Tovar wanted to create a computer program to diagnose the probability of certain personality types. With only answers from a few questions, the program was able to accurately diagnose the correct

Probabilistic reasoning in bayesian networks: A relational database approach
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Probabilistic reasoning in Bayesian networks is normally conducted on a junction tree by repeatedly applying the local propagation whenever new evidence is observed. In this paper, we suggest to treat probabilistic reasoning as database queries. We adapt a

Architectures integrating case-based reasoning and bayesian networks for clinical decision support
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In this paper we discuss different architectures for reasoning under uncertainty related to our ongoing research into building a medical decision support system. The uncertainty in the medical domain can be divided into a well understood part and a less understood part.

A Robot Group Control Based on Bayesian Reasoning
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ABSTRACT The objective of this paper is to discus the probabilistic part of the model for robot group control applied in industrial applications. The proposed model is based on well known concepts of Ubiquitous Computing and enables contextual perception of the

Bayesian reasoning based on predictive and contextual feature selection
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Abstract Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might

Training a Bayesian: Three-and-a-half-year-olds  Reasoning about Ambiguous Evidence
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Abstract Previous work has demonstrated the importance of both naïve theories and statistical evidence to children s causal reasoning. In particular, four-year-olds can use statistical evidence to update their beliefs. However, the story is more complex for three-

Bayesian Reasoning Method For Intelligence Using Natural Frequencies
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I would like to thank Professor Kristan Wheaton for his guidance, advice, and support throughout the thesis process. I would also like to thank Professor Hemangini Deshmukh for her help analyzing my findings and performing the statistical analysis, which I would have

A Bayesian Framework for Robust Reasoning from Sensor Networks
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ABSTRACT The work described in this paper defines a Bayesian framework to use noisy, but redundant data from multiple sensor streams and incorporate it with the contextual and domain knowledge that is provided by both the physical constraints imposed by the local

An OWL ontology and bayesian network to suport legal reasoning in the owners corporation domain
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Abstract. The paper describes the development of a legal decision support guide for owners corporation cases in the state of Victoria, Australia that uses an OWL ontology and Bayesian Network to perform legal reasoning. The rate of growth of owners corporations (also

Bayesian signal



Bayesian and empirical Bayesian approach to weighted averaging of ECG signal
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Abstract. One of the prime tool in non-invasive cardiac electrophysiology is the recording of an electrocardiographic signal (ECG) which analysis is greatly useful in the screening and diagnosis of cardiovascular diseases. However, one of the greatest problems is that

Bayesian estimation of time-frequency coefficients for audio signal enhancement
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Abstract The Bayesian paradigm provides a natural and effective means of exploiting prior knowledge concerning the time-frequency structure of sound signals such as speech and music something which has often been overlooked in traditional audio signal processing

Audio signal segmentation using recursive Bayesian change-point detectors
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Abstract:-Two basic types of change-point detectors are used for a localization of abrupt changes in audio signals, especially in speech and music signals. The Bayesian autoregressive change-point detector (BACD) and Bayesian polynomial change-point

Online learning algorithm of dynamic Bayesian networks for nonstationary signal processing
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Abstract. In this paper, we investigate a novel online estimation algorithm for dynamic Bayesian network (DBN) parameters, given as conditional probabilities. We sequentially update the parameter adjustment rule based on observation data. We apply our algorithm

Clarify some issues on the sparse Bayesian learning for sparse signal recovery
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Abstract:Sparse Bayesian learning (SBL) is an important family of algorithms for sparse signal recovery and compressed sensing. It has shown superior recovery performance in challenging practical problems, such as highly underdetermined inverse problems,

Bayesian change-points estimation applied to GPS signal tracking
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A hierarchical Bayesian model is applied to off-line segmentation of the GPS signal discriminator. The purpose of this work is to estimate the code delay of the receiving GPS CDMA code in order to retime the local receiver code and to estimate the pseudorange

Empirical Bayesian averaging method and its application to noise reduction in ECG signal
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An electrocardiogram (ECG) is the prime tool in non-invasive cardiac electrophysiology and has a prime function in the screening and diagnosis of cardiovascular diseases. However one of the greatest problems is that usually recording an electrical activity of the heart is

Journal of Magnetic Resonance 88, 552-570 (1990) Bayesian Analysis. II. Signal Detection and Model Selection
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ABSTRACT. In the preceding paper, Bayesian analysis was applied to the parameter estimation problem, given quadrature NMR data. Here Bayesian analysis is extended to the problem ofselecting the model which is most probable in view of the data and all the prior

Bayesian estimation and tracking of dynamic signal subspaces
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Page 1. BAYESIAN ESTIMATION AND TRACKING OF DYNAMIC SIGNAL SUBSPACES Anuj Srivastava Department of Statistics Florida State University SPAWC, Tuesday, July 13 th Lisbon. Page 2. TOOLS FROM DIFFERENTIAL GEOMETRY Quick Comments:

Progress in signal-based bayesian monitoring
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ABSTRACT Localization tools such as the CTBTO's GA (Global Association) system work by analyzing detections of arriving phases; these detections constitute a discretized, thresholded summary of information from the underlying seismic waveforms. By contrast,

Signal-BNF: a Bayesian network fusing approach to predict signal peptides
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A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge

Bayesian methods for music signal analysis
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Prior p (y) p () p (y)× p () y= 1 y= 2 y= 3 y= 4 y= 5 y= 6 = 1 1/36 1/36 1/36 1/36 1/36 1/36 = 2 1/36 1/36 1/36 1/36 1/36 1/36 = 3 1/36 1/36 1/36 1/36 1/36 1/36 = 4 1/36 1/36 1/36

The Theory of Signal Detectability: Bayesian Philosophy, Classical Statistics, and the Composite Hypothesis
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ABSTRACT Receiver design and performance from a Bayesian viewpoint depend upon a priori specification whenever unknown parameters are encountered in the detection situation; any available information is expressed in the form of an a priori density. A

Reconstructing condition-specific signal transduction hierarchy using Bayesian networks
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Bayesian networks (BNs) can simultaneously predict probabilistic causal relationships among biomolecules from noisy genome-wide measurements. However, optimal inference of Bayesian Network on the given data is a NP-hard problem. Consequently, existing BN

BAYESIAN PERIODIC SIGNAL DETECTION. 1. ANALYSIS OF 20 YEARS OF RADIO FLUX MEASUREMENTS OF THE X-RAY BINARY LS I+ 61 303
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ABSTRACT We extend the Bayesian method of Gregory and Loredo (1992) for the detection of a periodic signal of unknown shape and period, to the case where the noise sampling distribution is independent Gaussian. The analysis readily handles nonuniformily sampled

Bayesian approaches for robust array signal processing
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Abstract Modern techniques for sensor array signal processing are hampered by the often unrealistic and overly simplistic assumptions used in their development. Foremost among these assumptions are those relating to the array geometry or response, including for

Bayesian signal separation applied to ground-roll removal
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Page 1. Bayesian signal separation applied to ground-roll removal Two adaptive separation schemes Many Problems Carson Yarham Page 2. Introduction Introduction Adaptive Subtraction Schemes Sparsity Block Coordinate Relaxation (BCR) Bayesian Formulation

Machine Learning Framework for Bayesian Signal Processing
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Abstract In this thesis, we present a Bayesian machine learning framework for signal processing, based on variational message passing. The developed framework and its open- source software implementation allow building generative models using a set of building

Release the BEESTS: Bayesian Estimation of Stop-Signal Reaction Time Distributions
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Abstract The stop-signal paradigm is frequently used to study response inhibition. In this paradigm, participants perform a two-choice response time task where the primary task is occasionally interrupted by a stop-signal that prompts participants to withhold their

Statistical Signal Processing Bayesian Parameter Estimation Using Single-Bit Dithered Quantization.. G. Zeitler, G. Kramer, and AC Singer
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REGULAR PAPERS Statistical Signal Processing Bayesian Parameter Estimation Using Single-Bit Dithered Quantization

An Introduction to Bayesian Methods in Signal Processing
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Learning: For a given set of realizations from some random variables, learn a model to best describe the data. Example: Observations: X=[x1, x2, xN] Model: learnsuch that f (x| ) is

BOOTSTRAPPED SPARSE BAYESIAN LEARNING FOR SPARSE SIGNAL RECOVERY
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ABSTRACT In this article we study the sparse signal recovery problem in a bayesian framework using a novel Bootstrapped Sparse Bayesian Learning method. Sparse Bayesian Learning (SBL) framework is an effective tool for pruning out the irrelevant features and

An Experimental Study of Information Overload in Financial Bayesian Weighting Tasks of Varying Signal Loads
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Abstract: Information overload, here, refers to the act of assimilating less information in a task that contains strictly more relevant information, but is otherwise identical. We test for information overload experimentally, with subjects estimating the expected prices of

Research on Signal Detection Method of High Precision Based on Bayesian Fusion of Multisensory System
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Faced to low detection rate and low credibility of single sensor because of noise, this paper proposes Bayesian fusion based on multisensory system, whose detec tion rate and credibility are discussed. After simulation, it is concluded that the Bayesian fusion is a

BAYESIAN APPROACH TO CLASSIFIER DESIGN WITH APPLICATION TO QRS DETECTION IN ECG SIGNAL
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The paper presents application of Bayesian approach to design of kernel based classifier. The classification function is constructed using the probability distribution function of standard normal distribution and independent gaussian random variables. The

Bayesian or Laplacien inference, entropy and information theory and information geometry in data and signal processing
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Abstract. The main object of this tutorial article is first to review the main inference tools using Bayesian approach, Entropy, Information theory and their corresponding geometries. This review is focused mainly on the ways these tools have been used in data, signal and

BAYESIAN PERIODIC SIGNAL DETECTION. 1. ANALYSIS OF 20 YEARS OF RADIO FLUX MEASUREMENTS OF THE X-RAY BINARY LS I
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ABSTRACT We extend the Bayesian method of Gregory and Loredo 1992 for the detection of a periodic signal of unknown shape and period, to the case where the noise sampling distribution is independent Gaussian. The analysis readily handles nonuniformily sampled

Modeling, simulation and signal processing for Control room EMI problem using frequency domain Approach and Bayesian estimation
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ABSTRACT The problem of separation of mixed signal spectrum is encountered in many engineering systems. One such problem is a problem of electromagnetic topology wherein mixed signals at spatial points are encountered and one would like to separate them out

Applying Bayesian Inference for Signal Extraction Model of Time Series
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Abstract This paper explores a way to apply Bayesian Inference to Signal Extraction model of time series. The basic idea is to treat hyperparameters in stochastic time series models of the components as a random vector and compute a posterior density function using

The Impact of Prior Assumptions on Bayesian Estimates of Inflation Parameters and the Expected Gravitational Waves Signal from Inflation
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Shortly after its introductioninflation was found to produce a nearly flat Gaussian spectrum of adiabatic density perturbations that could have been the seeds of observed structure in the Universe . The simplest model of inflation

Bayesian decomposition trees with application to signal denoising
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ABSTRACT Tree-structured dictionaries of orthonormal bases (wavelet packet/Malvar's wavelets) provide a natural framework to answer the problem of nding a\ best representation" of both deterministic and stochastic signals. In this paper, we reformulate

Extracting a common signal in Northern Quebec black spruce tree-rings with a Bayesianhierarchical model
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Dendrochronology, the scientific dating method based on the analysis of tree-ring growth patterns, has been frequently applied in climatology. The basic premise of dendroclimatology is that tree rings can be viewed as climate proxies, ie rings are

On Self-Consistency of Cost Functions for Blind Signal Processing Based on NeutralBayesian Estimators
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Abstract In some blind signal processing tasks, such as blind source deconvolution and blind source separation, the optimal signal processing structure is designed adaptively through cost function optimization. A class of cost functions known in the literature is

Supplemental material for BEESTS: Bayesian Estimation of Stop-Signal Reaction Time Distributions
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This section presents a comparison of the parameter estimates from the BEESTS and the WinBUGS implementation of the Bayesian parametric approach . We fit the stop–signal data of 20 participants

Estimating the periodic components of a biomedical signal through inverse problem modelling and Bayesian inference with sparsity enforcing prior
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Abstract. The recent developments in chronobiology need a periodic components variation analysis for the signals expressing the biological rhythms. A precise estimation of the periodic components vector is required. The classical approaches, based on FFT methods

Signal Estimation From Samples With Location Errors.. S. Kumar, V. K Goyal, and SE Sarma 5285 Sparse Reconstruction Using Distribution Agnostic Bayesian
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A Tighter Uncertainty Principle for Linear Canonical Transform in Terms of Phase Derivative

Signal discovery in sparse spectra: a Bayesian analysis
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Abstract A Bayesian analysis of the probability of a signal in the presence of background is described. As an example, the method was used to calculate the sensitivity of the GERDA experiment to neutrinoless double beta decay. In addition, we discuss the use of

Bayesian Computer intensive methods for statistical signal processing
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In cases where this cannot be achieved analytically the approximation problem can be tackled indirectly, as it is often possible to generate random samples from p (x), ie by

Characteristic points detection in ECG signal using Bayesian learning and fuzzy system
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Characteristic points detection such as beginnings and ends of P-wave, T-wave or QRS complex is one of primary aims in automated analysis of ECG signal. The paper presents one possible approach based on Bayesian inference to design of kernel based classifier.

BAYESIAN DETECTION AND LOCALIZATION OF SIGNAL CHANGES
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ABSTRACT The computationally effective and numerical stable recursive autoregressive Bayesian changepoint detector is suggested. The statistical evaluation of its precision performed on synthetic signals is described together with chosen illustration of signal

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