Bayesian Data Science: The What, Why, and How
Bayesian data science offers a powerful framework for incorporating prior knowledge into statistical analysis, improving predictions, and informing decisions in a probabilistic manner.
Recent posts
Julia Robinson: Mathematician and Pioneer in Decision Problems
Julia Robinson was a trailblazing mathematician known for her work on decision problems and number theory. She played a crucial role in solving Hilbert’s Tenth Problem and became the first woman elected to the National Academy of Sciences.
Introduction to Partial Differential Equations (PDEs) from a Data Science Perspective
PDEs offer a powerful framework for understanding complex systems in fields like physics, finance, and environmental science. Discover how data scientists can integrate PDEs with modern machine learning techniques to create robust predictive models.
Understanding Ordinal Regression: A Comprehensive Guide
Explore the architecture of ordinal regression models, their applications in real-world data, and how marginal effects enhance the interpretability of complex models using Python.
Katherine Johnson: The Mathematician Who Helped Launch America into Space
Katherine Johnson was a trailblazing mathematician at NASA whose calculations for the Mercury and Apollo missions helped guide U.S. space exploration. Learn about her groundbreaking contributions to applied mathematics.
The Role of Data Science in Predictive Maintenance
Learn how data science revolutionizes predictive maintenance through key techniques like regression, anomaly detection, and clustering to forecast machine failures and optimize maintenance schedules.
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.