Search results for "Categorical time series"

showing 2 items of 2 documents

Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data

2019

Summary In food science, it is of great interest to obtain information about the temporal perception of aliments to create new products, to modify existing products or more generally to understand the mechanisms of perception. Temporal dominance of sensations is a technique to measure temporal perception which consists in choosing sequentially attributes describing a food product over tasting. This work introduces new statistical models based on finite mixtures of semi-Markov chains to describe data collected with the temporal dominance of sensations protocol, allowing different temporal perceptions for a same product within a population. The identifiability of the parameters of such mixtur…

futureStatistics and ProbabilityFOS: Computer and information sciencesGamma distributionmiceComputer sciencemedia_common.quotation_subjectPopulationdominancecomputer.software_genreStatistics - Applications01 natural sciencesMethodology (stat.ME)modelsExpectation-maximization algorithmModel-based clustering010104 statistics & probability0404 agricultural biotechnology[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Bayesian information criterionPerceptionExpectation–maximization algorithmApplications (stat.AP)Temporal dominance of sensations[MATH]Mathematics [math]0101 mathematicseducationStatistics - Methodologymedia_common2. Zero hungereducation.field_of_studyMarkov chainMarkov renewal processStatistical model04 agricultural and veterinary sciencesidentifiabilityMixture modelBayesian information criterion040401 food science[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]IdentifiabilityPenalized likelihoodData miningStatistics Probability and UncertaintycomputertdsCategorical time seriessensations
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Mixture Hidden Markov Models for Sequence Data: The seqHMM Package in R

2019

Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariate…

FOS: Computer and information sciencesStatistics and ProbabilityMultivariate statisticssequence analysisaikasarjatComputer sciencerMarkov modelStatistics - ComputationStatistics - Applications01 natural sciencesUnobservablecategorical time seriesR-kieli010104 statistics & probabilitymulti-channel sequences; categorical time series; visualizing sequence data; visualizing models; latent Markov models; latent class models; RCovariateApplications (stat.AP)Sannolikhetsteori och statistikComputer software0101 mathematicsTime seriesProbability Theory and StatisticsHidden Markov modelCluster analysislcsh:Statisticslcsh:HA1-4737Categorical variableComputation (stat.CO)ta112business.industryvisualizing sequence dataR (programming languages)Pattern recognitionmulti-channel sequencesvisualizing modelslatent class modelssekvenssianalyysiArtificial intelligencelatent markov modelstime seriesStatistics Probability and UncertaintybusinessSoftwareJournal of Statistical Software
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