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Last time I discussed the HMC (Hamiltonian Monte Carlo or Hybrid Monte Carlo) algorithm that forms the basis of Stan and I likened the algorithm to kicking a marble around in a bucket. The multi-dimensional shape of the bucket is defined by minus the log-posterior and the marble represents the current value of the estimator. We give […]
In the last few postings I have described how the Bayesian analysis program, Stan, can be called from within Stata. Before we can go any further we have to understand how Stan works, which is to say we need to understand the basics of Hamiltonian Monte Carlo, as this is Stan’s way of sampling from the posterior. I’ll start […]