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Bayesian inference for stochastic processes /

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"The book aims to introduce Bayesian inference methods for stochastic processes. The Bayesian approach has advantages compared to non-Bayesian, among which is the optimal use of prior information via data from previous similar experiments. Examples from biology, economics, and astronomy reinfor...

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Bibliographic Details
Main Author: Broemeling, Lyle D. (Author)
Corporate Author: Taylor & Francis
Format: eBook
Language:English
Published: Boca Raton, FL : CRC Press, [2018]
Edition:First edition.
Subjects:
Probabilities.
Bayesian statistical decision theory.
Stochastic processes.
Probability
Stochastic Processes
Probabilités.
Théorie de la décision bayésienne.
Processus stochastiques.
probability.
MATHEMATICS > Applied.
MATHEMATICS > Probability & Statistics > General.
Bayesian statistical decision theory
Probabilities
Stochastic processes
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Introduction to Bayesian inference for stochastic processes
  • Bayesian analysis
  • Introduction to stochastic processes
  • Bayesian inference for discrete Markov chains
  • Examples of Markov chains in biology
  • Inferences for Markov chains in continuous time
  • Bayesian inference: examples of continuous-time Markov chains
  • Bayesian inferences for normal processes
  • Queues and time series.