Gaussian Processes for Time-Series Analysis in Python
Dive into Gaussian Processes for time-series analysis using Python, combining flexible modeling with Bayesian inference for trends, seasonality, and noise.
Recent posts
Customer Lifetime Value: An In-Depth Exploration for Data Practitioners and Marketers
A detailed exploration of Customer Lifetime Value (CLV) for data practitioners and marketers, including its calculation, prediction, and integration with other business data.
Understanding Value at Risk (VaR) and Its Types
A detailed exploration of Value at Risk (VaR), covering its different types, methods of calculation, and applications in modern portfolio management.
Understanding Mean Time Between Failures (MTBF)
Explore the key concepts of Mean Time Between Failures (MTBF), how it is calculated, its applications, and its alternatives in system reliability.
Advanced Statistical Methods for Efficient A/B Testing
An in-depth exploration of sequential testing and its application in A/B testing. Understand the statistical underpinnings, advantages, limitations, and practical implementations in R, JavaScript, and Python.
Walking the Mathematical Path
Dive into the fascinating world of pedestrian behavior through mathematical models like the Social Force Model. Learn how these models inform urban planning, crowd management, and traffic control for safer and more efficient public spaces.
The Role of Error Terms in Multiple Linear Regression and Binary Logistic Regression
Delve into how multiple linear regression and binary logistic regression handle errors. Learn about explicit and implicit error terms and their impact on model performance.
Understanding PCA: A Step-by-Step Guide to Principal Component Analysis
Learn about Principal Component Analysis (PCA) and how it helps in feature extraction, dimensionality reduction, and identifying key patterns in data.
Simpson’s Paradox: Theoretical Foundations and Implications in Data Analysis
Simpson’s Paradox shows how aggregated data can lead to misleading trends. Learn the theory behind this paradox, its practical implications, and how to analyze data rigorously.
Probability Distributions in Machine Learning
Understand key probability distributions in machine learning and their applications, including Bernoulli, Gaussian, and Beta distributions.