
Explains the Generalized Bass Model (GBM), incorporating advertising and price cuts into new product diffusion forecasting, with Python simulations.

This blog provides detailed explanations of Python implementations and the mathematical theories behind them. It introduces algorithms and techniques step by step, then explores the mathematical principles behind them. For example, machine learning articles use Python code to demonstrate implementation details and explain how and why the algorithms work from a mathematical perspective. Fundamental concepts such as linear algebra, calculus, and probability theory are also explained with concrete examples.
Diagrams and graphs make each implementation step easier to understand, and links to related papers and reference materials provide further context. The goal is to help readers understand both the Python implementations and their underlying theory, then apply that knowledge to their own projects.
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Explains the Generalized Bass Model (GBM), incorporating advertising and price cuts into new product diffusion forecasting, with Python simulations.

Explains the Gumbel distribution, a core concept in extreme value statistics, with Python-based parameter estimation, visualization, and the Gumbel-Max Trick.

Explains the theoretical background of the Bass Diffusion Model for predicting new product adoption, with Python simulations and fitting to sample data.

Compares the Bass Model for new product diffusion with the SIR model for infectious disease spread, explaining their mathematical similarities and differences.