A Gentle Introduction to Monte Carlo Sampling for Probability

Monte Carlo methods are a class of techniques for randomly sampling a probability distribution.

There are many problem domains where describing or estimating the probability distribution is relatively straightforward, but calculating a desired quantity is intractable. This may be due to many reasons, such as the stochastic nature of the domain or an exponential number of random variables.

Instead, a desired quantity can be approximated by using random sampling, referred to as Monte Carlo methods. These methods were initially used around the time that the first computers were created and remain pervasive through all fields of science and engineering, including artificial intelligence and machine learning.

In this post, you will discover Monte Carlo methods for sampling probability distributions.

After reading this post, you will know:

  • Often, we cannot calculate a desired quantity in probability, but we can define the probability distributions for the random variables directly or indirectly.
  • Monte Carlo sampling a class of methods for randomly sampling from a probability distribution.
  • Monte Carlo sampling provides the foundation for many machine learning methods such as resampling, hyperparameter tuning, and ensemble learning.

Kick-start your project with my new book Probability for Machine Learning, including step-by-step tutorials and the Python
To finish reading, please visit source site