方法证据记录
Time series MCMC
Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics, trends, and seasonal patterns across every time point.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Markov Chain Monte Carlo for Time Series Models
分类方法记录 · bayesian / bayesian
- Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553. · DOI 10.1093/biomet/81.3.541
- West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. · ISBN 978-0387947259
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