Markov chain
Beyond Market Intelligence keeps Markov chain in one place: 2 stories so far. The section currently leads with “Model Markov Chains in Python with NumPy for Smarter Data Workflows” and “Explore a probabilistic path to understanding Hamiltonian Monte Carlo”. A three-state writing workflow, drafting, revising, and publishing, makes an ideal test case for understanding Markov chains. Hamiltonic Monte Carlo often arrives wrapped in physics metaphors that obscure as much as they reveal. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Markov chain story on Beyond Market Intelligence, newest first.
Model Markov Chains in Python with NumPy for Smarter Data Workflows
A three-state writing workflow, drafting, revising, and publishing, makes an ideal test case for understanding Markov chains. This guide walks through transition matrices, the Chapman, Kolmogorov equation, and stationary distributions using NumPy, turning abstract probability into practical Python. The approach is direct and instructive, not theoretical. For a different angle on mathematical clarity, our video "Explore embeddings through the geometry of cosine similarity explained simply" shows how dot products reveal structure in vector space. Both pieces share a commitment to making complex ideas feel manageable.
Explore a probabilistic path to understanding Hamiltonian Monte Carlo
Hamiltonic Monte Carlo often arrives wrapped in physics metaphors that obscure as much as they reveal. In these notes, the author strips that away, building HMC from a purely probabilistic foundation. Starting with an auxiliary variable and the construction of a proper Markov chain, the exposition moves through Hamiltonian dynamics, leapfrog integration, and the critical properties of reversibility and volume preservation. The result is a clearer path to understanding *why* HMC works, not just *how* to apply it.