Paths of Combinatorics and Probability
Dive into the intersection of combinatorics and probability, exploring how these fields work together to solve problems in mathematics, data science, and beyond.
Recent posts
Mastering Combinatorics with Python
A practical guide to mastering combinatorics with Python, featuring hands-on examples using the itertools library and insights into scientific computing and probability theory.
Distinguishing Ergodic Regimes from Processes
An in-depth look into ergodicity and its applications in statistical analysis, mathematical modeling, and computational physics, featuring real-world processes and Python simulations.
Elegance of the Pigeonhole Principle: A Mathematical Odyssey
A journey into the Pigeonhole Principle, uncovering its profound simplicity and exploring its applications in fields like combinatorics, number theory, and geometry.
The Power of Dimensionality Reduction
A comprehensive guide to spectral clustering and its role in dimensionality reduction, enhancing data analysis, and uncovering patterns in machine learning.
Mysteries of Clustering
Discover the inner workings of clustering algorithms, from K-Means to Spectral Clustering, and how they unveil patterns in machine learning, bioinformatics, and data analysis.
Convergence of Topology and Data Science
Dive into Topological Data Analysis (TDA) and discover how its methods, such as persistent homology and the mapper algorithm, help uncover hidden insights in high-dimensional and complex datasets.
Understanding Customer Lifetime Value
Discover the importance of Customer Lifetime Value (CLV) in shaping business strategies, improving customer retention, and enhancing marketing efforts for sustainable growth.
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.
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.