Markov Chains help us to predict future events based on the current state as the only parameter. It does not depend on history to reach the current state. Markov Chains have extensive use in Physics and material sciences. Monte Carlo simulations are used for predicting situations through repeated simulations. It provides us with a probability […]
Today we look at a very famous method called the Monte Carlo, which can be used to solve any problem having a probabilistic interpretation.
In this article, we will be learning about how to do a Monte-Carlo Simulation of a simple random experiment in Python. Note: Monte Carlo Simulation is a mathematically complex field. So we have not gone into the details of the MC. Instead, we have used some intuitions and examples to understand the need and implementation
Monte-Carlo Simulation to find the probability of Coin toss in python Read More »
In this article, let us try to compare deterministic vs Stochastic approaches to Machine Learning.
Deterministic vs Stochastic – Machine Learning (Fundamentals) Read More »
In this post, we will learn about the Cholesky decomposition, definitions of the terms related to this decomposition, the syntax for the function used to implement Cholesky decomposition, and a few examples. What is Matrix Decomposition? In linear algebra, matrix decomposition or matrix factorization refers to breaking the matrix down into two or more constituent
How to Compute Cholesky Decomposition of a Matrix? Read More »
Probability helps us with making investment choices. Since we can’t foresee the future, investors have to go on likelihood and chance instead of certainty. So before an analyst suggests a stock, they’ll gauge the probability it will gain or lose value. In this article, we’ll look at different types of probability distributions – discrete and
Understanding Probability Density and Distribution Functions Read More »