Biomaterial Database

Exact likelihood evaluation in a Markov mixture model for time series of seizure counts. 1992

N D Le, and B G Leroux, and M L Puterman

Department of Statistics, University of British Columbia, Vancouver, Canada.

This paper provides an alternative to Albert's (1991), Biometrics 47, 1371-1381) approximation to the E-step when using the EM algorithm for parameter estimation in Markov mixture models. Use of a recursive algorithm of Baum et al. (1970, Annals of Mathematical Statistics 41, 164-171) results in exact evaluation of the likelihood, optimal parameter estimates, and very efficient computation. Applications to time series of seizure counts and fetal movements clearly show the advantages of this exact approach.

UI	MeSH Term	Description	Entries
D007223	Infant	A child between 1 and 23 months of age.	Infants
D008390	Markov Chains	A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system.	Markov Process,Markov Chain,Chain, Markov,Chains, Markov,Markov Processes,Process, Markov,Processes, Markov
D009068	Movement	The act, process, or result of passing from one place or position to another. It differs from LOCOMOTION in that locomotion is restricted to the passing of the whole body from one place to another, while movement encompasses both locomotion but also a change of the position of the whole body or any of its parts. Movement may be used with reference to humans, vertebrate and invertebrate animals, and microorganisms. Differentiate also from MOTOR ACTIVITY, movement associated with behavior.	Movements
D005333	Fetus	The unborn young of a viviparous mammal, in the postembryonic period, after the major structures have been outlined. In humans, the unborn young from the end of the eighth week after CONCEPTION until BIRTH, as distinguished from the earlier EMBRYO, MAMMALIAN.	Fetal Structures,Fetal Tissue,Fetuses,Mummified Fetus,Retained Fetus,Fetal Structure,Fetal Tissues,Fetus, Mummified,Fetus, Retained,Structure, Fetal,Structures, Fetal,Tissue, Fetal,Tissues, Fetal
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001699	Biometry	The use of statistical and mathematical methods to analyze biological observations and phenomena.	Biometric Analysis,Biometrics,Analyses, Biometric,Analysis, Biometric,Biometric Analyses
D012640	Seizures	Clinical or subclinical disturbances of cortical function due to a sudden, abnormal, excessive, and disorganized discharge of brain cells. Clinical manifestations include abnormal motor, sensory and psychic phenomena. Recurrent seizures are usually referred to as EPILEPSY or "seizure disorder."	Absence Seizure,Absence Seizures,Atonic Absence Seizure,Atonic Seizure,Clonic Seizure,Complex Partial Seizure,Convulsion,Convulsions,Convulsive Seizure,Convulsive Seizures,Epileptic Seizure,Epileptic Seizures,Generalized Absence Seizure,Generalized Tonic-Clonic Seizures,Jacksonian Seizure,Myoclonic Seizure,Non-Epileptic Seizure,Nonepileptic Seizure,Partial Seizure,Seizure,Seizures, Convulsive,Seizures, Focal,Seizures, Generalized,Seizures, Motor,Seizures, Sensory,Tonic Clonic Seizure,Tonic Seizure,Tonic-Clonic Seizure,Atonic Absence Seizures,Atonic Seizures,Clonic Seizures,Complex Partial Seizures,Convulsion, Non-Epileptic,Generalized Absence Seizures,Myoclonic Seizures,Non-Epileptic Seizures,Nonepileptic Seizures,Partial Seizures,Petit Mal Convulsion,Seizures, Auditory,Seizures, Clonic,Seizures, Epileptic,Seizures, Gustatory,Seizures, Olfactory,Seizures, Somatosensory,Seizures, Tonic,Seizures, Tonic-Clonic,Seizures, Vertiginous,Seizures, Vestibular,Seizures, Visual,Single Seizure,Tonic Seizures,Tonic-Clonic Seizures,Absence Seizure, Atonic,Absence Seizure, Generalized,Absence Seizures, Atonic,Absence Seizures, Generalized,Auditory Seizure,Auditory Seizures,Clonic Seizure, Tonic,Clonic Seizures, Tonic,Convulsion, Non Epileptic,Convulsion, Petit Mal,Convulsions, Non-Epileptic,Focal Seizure,Focal Seizures,Generalized Seizure,Generalized Seizures,Generalized Tonic Clonic Seizures,Generalized Tonic-Clonic Seizure,Gustatory Seizure,Gustatory Seizures,Motor Seizure,Motor Seizures,Non Epileptic Seizure,Non Epileptic Seizures,Non-Epileptic Convulsion,Non-Epileptic Convulsions,Olfactory Seizure,Olfactory Seizures,Partial Seizure, Complex,Partial Seizures, Complex,Seizure, Absence,Seizure, Atonic,Seizure, Atonic Absence,Seizure, Auditory,Seizure, Clonic,Seizure, Complex Partial,Seizure, Convulsive,Seizure, Epileptic,Seizure, Focal,Seizure, Generalized,Seizure, Generalized Absence,Seizure, Generalized Tonic-Clonic,Seizure, Gustatory,Seizure, Jacksonian,Seizure, Motor,Seizure, Myoclonic,Seizure, Non-Epileptic,Seizure, Nonepileptic,Seizure, Olfactory,Seizure, Partial,Seizure, Sensory,Seizure, Single,Seizure, Somatosensory,Seizure, Tonic,Seizure, Tonic Clonic,Seizure, Tonic-Clonic,Seizure, Vertiginous,Seizure, Vestibular,Seizure, Visual,Seizures, Generalized Tonic-Clonic,Seizures, Nonepileptic,Sensory Seizure,Sensory Seizures,Single Seizures,Somatosensory Seizure,Somatosensory Seizures,Tonic Clonic Seizures,Tonic-Clonic Seizure, Generalized,Tonic-Clonic Seizures, Generalized,Vertiginous Seizure,Vertiginous Seizures,Vestibular Seizure,Vestibular Seizures,Visual Seizure,Visual Seizures
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016013	Likelihood Functions	Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.	Likelihood Ratio Test,Maximum Likelihood Estimates,Estimate, Maximum Likelihood,Estimates, Maximum Likelihood,Function, Likelihood,Functions, Likelihood,Likelihood Function,Maximum Likelihood Estimate,Test, Likelihood Ratio

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