Abstract
In Chapter 8, we introduced the fundamental ideas of Bayesian inference, in which prior distributions on parameters are used together with data to obtain posterior distributions and thus interval estimates of parameters. However, in practice, Bayesian posterior distributions are often difficult to compute.
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Suess, E.A., Trumbo, B.E. (2010). Using Gibbs Samplers to Compute Bayesian Posterior Distributions. In: Introduction to Probability Simulation and Gibbs Sampling with R. Use R, vol 0. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68765-0_9
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DOI: https://doi.org/10.1007/978-0-387-68765-0_9
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