Sampling Methods in Numpy

This repository contains code for some basic sampling methods implemented using numpy.

The following methods are implemented with examples

• Importance Sampling (Univariate example)
• Rejection Sampling (Univariate example)
• Metropolis-Hastings (Univariate and Multivariate example)
• Gibbs Sampling (Multivariate example)
• Langevin Monte Carlo
  • Unadjusted Langevin Algorithm (ULA) - Pytorch
  • Metropolis-adjusted Langevin Algorithm (MALA) - Pytorch
• Inverse Transform Sampling
  • Cauchy Distribution
  • Exponential Distribution
  • Gumbel Distribution