Monte Carlo Approximation Methods: Which one should you choose and when?

Is it Inverse Transformation, Random Walk Metropolis-Hastings, or Gibbs? An analysis focusing on the mathematical foundation, Python implementation from scratch, and pros/cons of each method

Introduction to Approximation Sampling

For most probabilistic models of practical interest, exact inference is intractable, and so we have to resort to some form of approximation.

— Pattern Recognition and Machine Learning¹

Since deterministic inference is often intractable with probabilistic models as we saw just now, we now turn …

analysis approximation cons deep-dives foundation gibbs implementation inference machine learning markov-chains metropolis monte-carlo practical pros python random statistical-learning transformation