Monte_Carlo

Various simple and straightforward experiments with the Metropolis-Hastings algorithm and ordinary Monte Carlo methods. Some highlights:

The performance of Metropolis-Hastings algorithm is and to .
If X and Y are two independent exponentially distributed random variables with parameter 1, then X / (X Y) is uniformly distributed over the open interval (0,1). This analytically derived .
with added comparison of execution time differences between programming in basic style Python versus NumPy style Python.

Name		Name	Last commit message	Last commit date
Latest commit History 44 Commits
.gitignore		.gitignore
Metropolis-Hastings_exponential.ipynb		Metropolis-Hastings_exponential.ipynb
Metropolis-Hastings_multinormal.ipynb		Metropolis-Hastings_multinormal.ipynb
Metropolis-Hastings_statistical_tests.ipynb		Metropolis-Hastings_statistical_tests.ipynb
Monte_Carlo_integration_extremely_simple_example.ipynb		Monte_Carlo_integration_extremely_simple_example.ipynb
Monte_Carlo_transformation_of_random_variables.ipynb		Monte_Carlo_transformation_of_random_variables.ipynb
README.md		README.md
Verify_RNGs_with_lambdas.ipynb		Verify_RNGs_with_lambdas.ipynb

Provide feedback