MATLAB Implementation of Monte Carlo Simulation Algorithm

Monte Carlo method, also known as statistical simulation method or random sampling technique, is a stochastic simulation approach based on probability and statistical theory. It employs random numbers (or more commonly pseudo-random numbers) to solve various computational problems. This method connects the target problem with a specific probability model and uses computer statistical simulation or sampling to obtain approximate solutions. Key implementation aspects include random number generation using functions like rand() or randn(), probability distribution modeling, and iterative sampling processes.

Logistic Regression (LR) Classifier in Machine Learning with MATLAB Implementation

A comprehensive MATLAB implementation of Logistic Regression (LR) classifier for machine learning, featuring detailed source code and four experimental datasets for practical validation

Expectation-Maximization (EM) Algorithm for Probabilistic Models

In statistical computing, the Expectation-Maximization (EM) algorithm is an iterative method for finding maximum likelihood (MLE) or maximum a posteriori (MAP) estimates of parameters in probabilistic models that depend on unobserved latent variables. The EM algorithm is widely used in machine learning and computer vision for data clustering applications. The algorithm alternates between an expectation step (E-step), which computes the expected value of the latent variables given current parameters, and a maximization step (M-step), which updates parameters to maximize the expected log-likelihood.

Professional MATLAB source code with comprehensive documentation, examples, and detailed implementation guide.

MATLAB Code Implementation for Monte Carlo Simulation

Comprehensive Guide to Implementing Monte Carlo Simulations Using MATLAB Programming Techniques

MATLAB HMM Toolbox Implementation

MATLAB Implementation of Hidden Markov Model (HMM) Toolbox with Core Functions

MATLAB Implementation of Naive Bayes Classification

MATLAB Implementation of Naive Bayes Classification with Probability Calculations and Classification Decision Mechanisms

Particle Filter: A Robust Tracking Algorithm

Particle Filter: A Powerful Tracking Algorithm with Probabilistic Modeling and Dynamic State Estimation

MATLAB Implementation of Probabilistic Principal Component Analysis (PPCA)

MATLAB Code Implementation for Probabilistic Principal Component Analysis (PPCA) Computation