Probability Theory Basics for Data Science
An introduction to probability theory concepts every data scientist should know.
Recent posts
Machine Learning vs. Univariate Time Series Models in Predicting Emergency Department Visit Volumes
A comparison between machine learning models and univariate time series models for predicting emergency department visit volumes, focusing on predictive accuracy.
A Predictive Approach for Demand Forecasting in the Supply Chain Using Customer Behavior Modeling
Leveraging customer behavior through predictive modeling, the BG/NBD model offers a more accurate approach to demand forecasting in the supply chain compared to traditional time-series models.
Log-Rank Test in Survival Analysis: Comparing Survival Curves
The log-rank test is a key tool in survival analysis, commonly used to compare survival curves between groups in medical research. Learn how it works and how to interpret its results.
A Generalized Approach to Threshold Classification for Zero-Inflated Time Series Data Using Stationary Distributions
This article explores the use of stationary distributions in time series models to define thresholds in zero-inflated data, improving classification accuracy.
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.
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.