Data Science Numerical Analysis
The burn-in period is the initial phase in a Markov chain Monte Carlo (MCMC) simulation where the algorithm is allowed to run to ensure that it reaches a stable state before collecting data for analysis. During this time, the samples generated may not accurately represent the target distribution, as the algorithm needs time to 'forget' its starting values and converge to the desired distribution. Properly identifying the burn-in period is crucial for obtaining reliable results from MCMC methods.
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