Recurrence and Ergodicity for Exponential Family State-Space Models

Abstract

We give two results concerning the properties of state-space models with exponential family observation distribution and conjugate state distribution. The first result gives a simple and general interpretation of the parameters of the predictive state distribution in terms of the observation forecast distribution. The second result shows how the first result can be used to check the long-term model properties of recurrence and ergodicity for a class of non-Gaussian observation distributions. In particular, these results apply to models with Poisson, binomial and multinomial observation distributions. Keywords: Bayesian forecasting; Binomial time series; Multinomial time series; Poisson time series; Recursive updating; Time series. (KR)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1989
Accession Number
ADA213466

Entities

People

  • Adrian Raftery
  • Gary Grunwald
  • Peter Guttorp

Organizations

  • University of Washington

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Binomials
  • Delphi Method
  • Environmental Protection
  • Ergodic Processes
  • Filters
  • Kalman Filters
  • Markov Chains
  • Markov Processes
  • Maximum Likelihood Estimation
  • New York
  • Observation
  • Probability
  • Random Walk
  • Standards
  • Statistics
  • Stochastic Processes
  • Universities

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space