Description: In this course we present the basic principles of Bayesian statistics (an alternative to "orthodox" statistics). We start by learning how to estimate parameters for standard models (normal, binomial, Poisson) and then get acquainted with computational methods (MCMC) and software (WinBUGS) that can solve complicated problems that arise in real applications. Advanced topics include model comparison and decision theory.
File list (Check if you may need any files):
MAT-51706 Bayesian methods\BayesDental1.pdf
..........................\BayesDental2.pdf
..........................\GibbsDemo.m
..........................\lecture01.pdf
..........................\lecture02.pdf
..........................\lecture03.pdf
..........................\lecture04.pdf
..........................\lecture05.pdf
..........................\lecture06.pdf
..........................\lecture07.pdf
..........................\lecture08.pdf
..........................\lecture09.pdf
..........................\lecture10.pdf
..........................\lecture11.pdf
..........................\NewcombGibbs.m
..........................\notes01.pdf
..........................\notes02.pdf
..........................\notes03.pdf
..........................\notes04.pdf
..........................\notes05.pdf
..........................\notes06.pdf
..........................\notes07.pdf
..........................\notes08.pdf
..........................\notes09.pdf
..........................\notes10.pdf
..........................\notes11.pdf
..........................\set01.pdf
..........................\set02.pdf
..........................\set03.pdf
..........................\set04.pdf
..........................\set05.pdf
..........................\set06.pdf
..........................\set07.pdf
..........................\set08.pdf
..........................\set09.pdf
..........................\set10.pdf
..........................\set11.pdf
..........................\SI_Introduction.pdf
..........................\sol11.pdf
MAT-51706 Bayesian methods
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