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This repository contains implementations of some basic sampling methods in numpy.

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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

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This repository contains implementations of some basic sampling methods in numpy.

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