diff --git a/docs/source/sampler/metropolis_hastings.rst b/docs/source/sampler/metropolis_hastings.rst
index 6b2c868a41b7904003f11ae3637829873b464e18..cedb0b529569f0f5ac249ebac1ba5b45d1808491 100644
--- a/docs/source/sampler/metropolis_hastings.rst
+++ b/docs/source/sampler/metropolis_hastings.rst
@@ -1,6 +1,27 @@
 Metropolis Hastings Sampling
 ============================
 
+Metropolis Hastings sampling is a widely used Markov Chain Monte Carlo (MCMC)
+algorithm for generating a sequence of samples from a target probability
+distribution where direct sampling is difficult. The samples can be used to
+estimate properties of the target distribution, like its mean, variance, and higher
+moments. The samples can also be used to approximate the target distribution,
+for example via a histogram. The algorithm works by constructing a Markov chain
+with a stationary distribution equal to the target distribution. 
+
+Steps of the algorithm are as follows:
+
+  1. Choose an initial state for the Markov chain, usually from the target distribution.
+  2. At each iteration, propose a new state by sampling from a proposal distribution.
+  3. Calculate the acceptance probability.
+  4. Accept or reject the proposed state based on the acceptance probability.
+  5. Repeat steps 2-4 for a large number of iterations to obtain a sequence of samples.
+  6. Apply "burn-in" and "thining".
+
+Final samples from the Markov chain can then be used to estimate the target
+distribution or its properties.
+       
+
 MetropolisHastings Class
 ------------------------