Aggregated Markov Chain Research Articles

Estimation for aggregate models: The aggregate Markov chain

AbstractAn argument in favour of projecting the score function for models involving incomplete data is presented. Projection is then applied to the aggregate Markov‐chain model resulting in weighted least‐squares estimators. The limit theory and efficiency of these estimators are studied using martingale limit theory.

Computable Error Bounds for Aggregated Markov Chains

article Free AccessComputable Error Bounds for Aggregated Markov Chains Author: G. W. Stewart Department of Computer Science and Institute for Physical Science and Technology, The University of Maryland, College Park, MD Department of Computer Science and Institute for Physical Science and Technology, The University of Maryland, College Park, MDView Profile Authors Info & Claims Journal of the ACMVolume 30Issue 2April 1983 pp 271–285https://doi.org/10.1145/322374.322377Published:01 April 1983Publication History 41citation435DownloadsMetricsTotal Citations41Total Downloads435Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Properties of aggregated Markov chains

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Properties of aggregated Markov chains

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Two Problems in Markov Chains: A Topological Approach

This paper deals with two problems of asymptotic aggregation of an ergodic Markov chain. The first is concerned with the determination of an aggregated Markov chain, the asymptotic probabilities of which are just equal to conditional asymptotic probabilities defined on the original Markov chain. The second one relates to the determination of an aggregated chain, the asymptotic probabilities of which are equal to the asymptotic probabilities of the elements of a given state partition. Both problems are approached from a topological point of view and solutions are given in terms of signal-flow graph techniques. Comments about the main computational features of the method are also included.