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Bayesiansk statistik I

  • 7,5 hp

I den här kursen får du en introduktion till Bayesiansk analys, med fokus på förståelsen av grundläggande begrepp och metoder och enklare problem som studeras i detalj. Du får också på ett mer översiktligt plan lära dig att analysera mer komplicerade och realistiska problem.

After taking this course, you will understand the difference between various interpretations of probability and be able to formulate a statistical problem on the basis of a Bayesian perspective. You will both learn to solve standard statistical problems using Bayesian methods and to solve statistical problems using simulation-based computational methods, such as the Markov Chain Monte Carlo (MCMC), which are often used in Bayesian inference.

In Bayesian inference, parameters are considered to be random variables and any previous knowledge about these parameters is expressed as a probability distribution, the so called a priori distribution. This prior distribution is then updated to a posterior distribution by using Bayes’ theorem to combine it with the observed data which is expressed through the likelihood function. The a posterior distribution, thus, expresses evidence about the parameters after data has been observed.

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