Understanding Markov Chain Monte Carlo (MCMC)
This article delves into the fundamentals of Markov Chain Monte Carlo (MCMC), its applications, and its significance in solving complex, high-dimensional probability distributions.
Recent posts
Solving DSGE Models Numerically: Perturbation Techniques and Finite Difference Methods
A guide to solving DSGE models numerically, focusing on perturbation techniques and finite difference methods used in economic modeling.
Understanding Observational Error: Detailed Insights and Implications
Explore the different types of observational errors, their causes, and their impact on accuracy and precision in various fields, such as data science and engineering.
Mann-Whitney U Test vs. Independent T-Test: Non-Parametric Alternatives
The Mann-Whitney U test and independent t-test are used for comparing two independent groups, but the choice between them depends on data distribution. Learn when to use each and explore real-world applications.
Cochran’s Q Test: Comparing Three or More Related Proportions
Understand Cochran’s Q test, a non-parametric test for comparing proportions across related groups, and its applications in binary data and its connection to McNemar’s test.
A Comprehensive Guide to ARIMA Time Series Modeling
Learn the fundamentals of ARIMA modeling for time series analysis. This guide covers the AR, I, and MA components, model identification, validation, and its comparison with other models.
Ordinary Least Squares (OLS) Regression: Properties and Applications
Discover the foundations of Ordinary Least Squares (OLS) regression, its key properties such as consistency, efficiency, and maximum likelihood estimation, and its applications in linear modeling.
Analysis of the False Positive Rate (FPR) in Machine Learning
Learn what the False Positive Rate (FPR) is, how it impacts machine learning models, and when to use it for better evaluation.
Shapiro-Wilk Test vs. Anderson-Darling Test: Checking Normality in Data
Learn about the Shapiro-Wilk and Anderson-Darling tests for normality, their differences, and how they guide decisions between parametric and non-parametric statistical methods.
Understanding Prediction Error: Bias, Variance, and Model Evaluation Techniques
Learn about different methods for estimating prediction error, addressing the bias-variance tradeoff, and how cross-validation, bootstrap methods, and Efron & Tibshirani’s .632 estimator help improve model evaluation.