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README.md

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 # MCMC_library: Likelihood-Based/Likelihood-Free Metropolis-Hastings Algorithm
 
-MCMC_library is a pristine Python library that provides a comprehensive implementation of the Likelihood-Based and Likelihood-Free Metropolis-Hastings (MH) Algorithm.
+Welcome to the MCMC_library repository, a pristine Python library that offers a comprehensive implementation of the Likelihood-Based and Likelihood-Free Metropolis-Hastings (MH) Algorithm.
 
 ## Likelihood-Based MH Sampler
 
@@ -11,3 +11,25 @@ MCMC_library is a pristine Python library that provides a comprehensive implemen
 ![2D Mixture Gaussian 1](https://github.com/bominwang/MCMC_library/blob/main/Likelihood-based%20MHSampler/figure/mcmc2.png)
 ![2D Mixture Gaussian 2](https://github.com/bominwang/MCMC_library/blob/main/Likelihood-based%20MHSampler/figure/mcmc3.png)
 ![2D Mixture Gaussian 3](https://github.com/bominwang/MCMC_library/blob/main/Likelihood-based%20MHSampler/figure/mcmc4.png)
+
+## Getting Started
+
+The power of the Metropolis-Hastings algorithm is at your fingertips with just a single command:
+
+```python
+# Define the target_distribution, proposal_distribution, and other parameters
+# target_distribution: The target distribution you want to sample from.
+#                     It should be a function that calculates the probability density of a given sample.
+# proposal_distribution: The proposal distribution used to generate candidate samples from the current sample.
+#                        It determines how new samples are explored in the space.
+#                        Common choices include 'uniform' or 'normal', or you can provide your own function.
+# transfer_method: The transfer method specifying how to accept or reject candidate samples generated from the proposal distribution.
+#                  Common choices include 'component-wise' or 'block-wise'.
+# gpu_flag: Whether to enable GPU acceleration. If set to True, the algorithm will utilize GPU for computation (if available).
+samples = MetropolisHastings(target_distribution=target_distribution,
+                             proposal_distribution='uniform',
+                             transfer_method='component-wise',
+                             gpu_flag=False).run(initial_position=initial_state,
+                                                 steps_warm_up=10000,
+                                                 num_samples=10000,
+                                                 width=1.0, sigma=1.0)