Metropolis Algorithm
Explore the Metropolis algorithm as an efficient technique for sampling from non-normalized continuous probability distributions in C#. Understand how it uses Markov processes to generate samples, the role of initial and proposal distributions, and practical implementation details to tackle challenges like multi-modal distributions and sample correlation.
We'll cover the following...
We'll cover the following...
In the previous lesson, we implemented a technique for sampling from a non-normalized target PDF:
- Find an everywhere-larger helper PDF that we can sample from.
- Sample from it.
- Accept or reject the sample via a coin flip with the ratio of weights in the target distribution and the helper distribution.
This technique works, but it has a few drawbacks:
- It’s not at all clear how to find a suitable helper PDF without humans intervening.