Advanced Quantitative Methods
Markov Chain Monte Carlo (MCMC) is a class of algorithms used to sample from probability distributions when direct sampling is difficult. MCMC relies on constructing a Markov chain that has the desired distribution as its equilibrium distribution, allowing researchers to generate samples that approximate the target distribution. This technique is particularly useful in Bayesian analysis, where prior and posterior distributions play a crucial role in estimating parameters and testing hypotheses.
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