Gibbs Algorithm Step by Step 📘| introduction to machine learning #machinelearning #mlbasics

Have you ever struggled to sample from a complex, high-dimensional probability distribution? 🤯
In this video, we explain the Gibbs Algorithm (Gibbs Sampling) — a powerful Markov Chain Monte Carlo (MCMC) technique widely used in Bayesian Inference and Machine Learning.

Instead of computing a difficult joint probability distribution, Gibbs Sampling simplifies the process by sampling one variable at a time, conditioned on the current values of the other variables.

This video is perfect for B.Tech students studying Introduction to Machine Learning.

🎯What You Will Learn in This Video

What is the Gibbs Algorithm (Gibbs Sampling)
Why Gibbs Sampling is needed in Machine Learning
Difference between joint distribution vs conditional distribution
Step-by-step working of the Gibbs Algorithm
A simple, real-world example
How the algorithm reaches a stationary (converged) state
Clear diagram & flowchart explanation
Role of Gibbs Sampling in MCMC methods

Burn-in Period
Initial samples are discarded to allow the chain to converge.

Collection Phase
Remaining samples represent the target joint distribution.

💡 Why is Gibbs Sampling Important?

🔹 Special case of Metropolis-Hastings Algorithm
🔹 Backbone of many ML models
🔹 Used in:

Latent Dirichlet Allocation (LDA)

Bayesian Networks

Topic Modeling

Probabilistic Graphical Models

#machinelearning #btech #engineeringstudents #machinelearning

Видео Gibbs Algorithm Step by Step 📘| introduction to machine learning #machinelearning #mlbasics канала Btech_Decode