Mastering Bayesian Statistics: An In-Depth Guide to MCMC
Discover how Bayesian inference and MCMC algorithms like Metropolis-Hastings can solve complex probability problems through real-world examples and Python implementation.
Recent posts
Demystifying MCMC: A Practical Guide to Bayesian Inference
Explore Markov Chain Monte Carlo (MCMC) methods, specifically the Metropolis algorithm, and learn how to perform Bayesian inference through Python code.
A Closer Look at the Classic Bell Curve
Discover the significance of the Normal Distribution, also known as the Bell Curve, in statistics and its widespread application in real-world scenarios.
Marina Viazovska: Fields Medalist and Pioneer in Sphere Packing
Marina Viazovska won the Fields Medal in 2022 for her remarkable solution to the sphere packing problem in 8 dimensions and her contributions to Fourier analysis and modular forms.
Text Preprocessing Techniques for NLP in Data Science
Text preprocessing is a crucial step in NLP for transforming raw text into a structured format. Learn key techniques like tokenization, stemming, lemmatization, and text normalization for successful NLP tasks.
Mathematics of Machine Learning: A Comprehensive Exploration
This article delves into the core mathematical principles behind machine learning, including classification and regression settings, loss functions, risk minimization, decision trees, and more.
Comparing Value at Risk (VaR) and Expected Shortfall (ES): A Data-Driven Analysis
A comprehensive comparison of Value at Risk (VaR) and Expected Shortfall (ES) in financial risk management, with a focus on their performance during volatile and stable market conditions.
Introduction to Data Engineering: Processes, Skills, and Tools
This article explores the fundamentals of data engineering, including the ETL/ELT processes, required skills, and the relationship with data science.
Why Managing Data Science Like Engineering Leads to Failure
While engineering projects have defined solutions and known processes, data science is all about experimentation and discovery. Managing them in the same way can be detrimental.
Solving Data Drift Issues in Credit Risk Models: A Practical Example
A comprehensive exploration of data drift in credit risk models, examining practical methods to identify and address drift using multivariate techniques.