A Short Introduction to WinBUGS
نویسنده
چکیده
1 Why Bayesian analysis using MCMC is good for you 1. Has a very solid decision-theoretical framework 2. It is intuitive: a. Combines the prior distribution (prior beliefs and/or experience) with the likelihood (experiment) to obtain the posterior distribution (accumulated information). b. In this context knowledge is equivalent to distribution and knowledge precision can be quantified by the precision parameter. 3. It is exact. Asymptotic approximations are not used. The “plug–in” principle is avoided. 4. Newly developed numerical methods (MCMC) make computations tractable for practically all models. 5. Focus shifts from model estimation to model correctness. 6. WinBUGS
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تاریخ انتشار 2004