Bayesian methodsBayesian / computational
Dynamic Bayesian Inference
Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.
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Sources
- West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
- Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Dissertation, University of California, Berkeley. link ↗
Related methods
Referenced by
Dynamic Bayesian Model AveragingDynamic Metropolis-Hastings AlgorithmDynamic Monte Carlo SimulationDynamic Particle FilterDynamic Sequential Monte CarloKalman Filter with Measurement ErrorSequential Monte Carlo with Measurement ErrorSpatial Kalman FilterTime series approximate Bayesian computationTime series MCMC