Displaying similar documents to “An algorithm for a new method of change-point analysis in the independent Poisson sequence”

Two algorithms based on Markov chains and their application to recognition of protein coding genes in prokaryotic genomes

Małgorzata Grabińska, Paweł Błażej, Paweł Mackiewicz (2013)

Applicationes Mathematicae

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Methods based on the theory of Markov chains are most commonly used in the recognition of protein coding sequences. However, they require big learning sets to fill up all elements in transition probability matrices describing dependence between nucleotides in the analyzed sequences. Moreover, gene prediction is strongly influenced by the nucleotide bias measured by e.g. G+C content. In this paper we compare two methods: (i) the classical GeneMark algorithm, which uses a three-periodic...

The likelihood ratio test for the number of components in a mixture with Markov regime

Elisabeth Gassiat, Christine Keribin (2010)

ESAIM: Probability and Statistics

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We study the LRT statistic for testing a single population i.i.d. model against a mixture of two populations with Markov regime. We prove that the LRT statistic converges to infinity in probability as the number of observations tends to infinity. This is a consequence of a convergence result of the LRT statistic for a subproblem where the parameters are restricted to a subset of the whole parameter set.

Application of the Rasch model in categorical pedigree analysis using MCEM: I binary data

G. Qian, R. M. Huggins, D. Z. Loesch (2004)

Discussiones Mathematicae Probability and Statistics

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An extension of the Rasch model with correlated latent variables is proposed to model correlated binary data within families. The latent variables have the classical correlation structure of Fisher (1918) and the model parameters thus have genetic interpretations. The proposed model is fitted to data using a hybrid of the Metropolis-Hastings algorithm and the MCEM modification of the EM-algorithm and is illustrated using genotype-phenotype data on a psychological subtest in families...

Bottom-up learning of hierarchical models in a class of deterministic POMDP environments

Hideaki Itoh, Hisao Fukumoto, Hiroshi Wakuya, Tatsuya Furukawa (2015)

International Journal of Applied Mathematics and Computer Science

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The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such...

Maxwell-Kelvin model for highloft materials

Přívratská, Jana, Jirsák, Oldřich, Bharanitharan, R.

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Compression behaviour and elastic recovery of highloft materials are described by the Maxwell-Kelvin rheological model. We present an algorithm how to determine input parameters for this rheological model using experimental data.

Application of MCMC to change point detection

Jaromír Antoch, David Legát (2008)

Applications of Mathematics

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A nonstandard approach to change point estimation is presented in this paper. Three models with random coefficients and Bayesian approach are used for modelling the year average temperatures measured in Prague Klementinum. The posterior distribution of the change point and other parameters are estimated from the random samples generated by the combination of the Metropolis-Hastings algorithm and the Gibbs sampler.