Bayesian Inference of State Space Models: Kalman Filtering and Beyond
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Bayesian Inference of State Space Models provides a comprehensive introduction to Bayesian inference for state space models, including Kalman filtering and beyond.
It covers the latest developments in Bayesian inference for state space models, including particle filters, sequential Monte Carlo methods, and variational Bayes.
The book is written in a clear and concise style, with a focus on practical implementation.
It includes numerous examples and exercises to help readers understand the material.
Bayesian Inference of State Space Models is an essential resource for researchers and practitioners who want to learn about Bayesian inference for state space models.
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