Bayesian Inference Explained
Explore the fundamentals of Bayesian inference and how prior beliefs combine with data to form posterior conclusions.
Explore the fundamentals of Bayesian inference and how prior beliefs combine with data to form posterior conclusions.
Understand how simple linear regression models the relationship between two variables using a single predictor.
An introduction to probability theory concepts every data scientist should know.
A comparison between machine learning models and univariate time series models for predicting emergency department visit volumes, focusing on predictive accuracy.
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.
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.
This article explores the use of stationary distributions in time series models to define thresholds in zero-inflated data, improving classification accuracy.
This article delves into the fundamentals of Markov Chain Monte Carlo (MCMC), its applications, and its significance in solving complex, high-dimensional probability distributions.
A guide to solving DSGE models numerically, focusing on perturbation techniques and finite difference methods used in economic modeling.
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.