Essential Guide to Probability Models: Monte Carlo, Markov, Queueing & Bayesian Resources

To facilitate students' access to different categories of models, this article compiles recent model articles, summarizing content related to statistical and probability models.

1. Probability Models

1.1 Monte Carlo Simulation

Model Simulation 05 Monte Carlo Simulation Introduction

Model Simulation 07 Monte Carlo Simulation – Mathematical Ideas

Model Simulation 08 Monte Carlo Simulation – Definite Integral Calculation

Model Simulation 09 Monte Carlo Simulation – Applications in Probability

Model Simulation 10 Monte Carlo Simulation – Finding Global Optimum

Model Simulation 11 Monte Carlo Simulation – Inventory Management Problem

Model Simulation 12 Monte Carlo Simulation – Queuing Problem

Model Simulation 13 Monte Carlo Simulation – Component Parameter Design

1.2 Markov Process

Probability Model 15 Overview of Markov Chains

Simulation Model 04 Markov Chain Model

Probability Model 04 Markov Chain Model – Piano Sales Inventory Strategy

Probability Programming 20 Markov Sampling

Probability Programming 19 Non-Markov Sampling

1.3 Queueing Theory

Simulation Model 01 Basic Concepts of Queueing Theory

Simulation Model 02 Single-Server Queueing Model

Simulation Model 03 Multi-Server Queueing Model

1.4 Bayesian Methods

Mathematics | Probability Statistics 04 Bayes' Theorem

Model Prediction 07 Machine Learning – Naive Bayes Model

Probability Statistics 39 Bayesian Statistics – Single Parameter Inference

Probability Statistics 38 Bayes' Theorem and Statistical Inference

Model Prediction 69 Bayesian Personalized Ranking (BPR) Algorithm

Probability Statistics 30 PyMC3 Bayesian Regression

Probability Statistics 28 Bayesian Linear Regression

Bayesian Network Modeling – Predicting Crime Occurrence

Bayesian Methods – The Three-Doors Problem

More model articles are being added continuously; stay tuned.