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Hidden Markov Processes

English

By (author): M. Vidyasagar

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron-Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum-Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. The book also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored. See more
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A01=M. VidyasagarAdditionAge Group_UncategorizedAlgorithmAlmost surelyApplied mathematicsAuthor_M. Vidyasagarautomatic-updateBiologistBiologyBlock matrixCanonical formCardinalityCategory1=Non-FictionCategory=PBTCategory=PDECategory=PSComputationComputational biologyConditional probabilityConditional probability distributionConvergence of random variablesConvex combinationConvex functionCOP=United StatesCountable setDelivery_Delivery within 10-20 working daysDynamic programmingEigenvalues and eigenvectorsEmpirical distribution functionEntropy rateeq_isMigrated=2eq_non-fictioneq_scienceEquationEstimationExistential quantificationExpected valueFinite setGeneHidden Markov modelIndependent and identically distributed random variablesInstance (computer science)IntegerJoint probability distributionLanguage_EnglishLarge deviations theoryLikelihood functionMarkov chainMarkov chain Monte CarloMarkov modelMarkov processMarkov propertyMathematicsMoment-generating functionMonte Carlo methodNatural numberNonnegative matrixNotationNucleotidePA=AvailableParameterPermutationPrice_€50 to €100ProbabilityProbability distributionProbability measureProbability theoryProteinPS=ActiveQuantityRandom variableRate functionReal numberScientific notationSequence alignmentsoftlaunchState spaceStationary distributionStationary processStatisticStochastic matrixStochastic processSubsetSummationTheoremUpper and lower boundsWithout loss of generality
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Product Details
  • Weight: 567g
  • Dimensions: 152 x 235mm
  • Publication Date: 24 Aug 2014
  • Publisher: Princeton University Press
  • Publication City/Country: US
  • Language: English
  • ISBN13: 9780691133157

About M. Vidyasagar

M. Vidyasagar is the Cecil and Ida Green Chair in Systems Biology Science at the University of Texas, Dallas. His many books include Computational Cancer Biology: An Interaction Network Approach and Control System Synthesis: A Factorization Approach.

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