Bayesian inference
Bayesian inference at Beyond Market Intelligence is a file of 3 stories. The newest of them: “How Watching a Few Episodes Reveals the Host's Strategy”, “When Samplers Fail: Benchmarking Three Methods Against Ground Truth”, and “Explore a probabilistic path to understanding Hamiltonian Monte Carlo”. The "always switch" strategy in the Monty Hall problem isn't universal, it depends entirely on the host. Three samplers walk into a Bayesian inference problem, and all three fail, spectacularly. 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 Bayesian inference story on Beyond Market Intelligence, newest first.
How Watching a Few Episodes Reveals the Host's Strategy
The "always switch" strategy in the Monty Hall problem isn't universal, it depends entirely on the host. Watch a few episodes and you can infer which host you've got. That's the insight worth exploring: the math changes when the game show host isn't a neutral actor. It's a reminder that probabilities live inside human decisions, not just formulas. For a deeper look at how context reshapes statistical truths, our piece on Simpson's paradox pairs nicely with this one.
When Samplers Fail: Benchmarking Three Methods Against Ground Truth
Three samplers walk into a Bayesian inference problem, and all three fail, spectacularly. That's the honest, refreshing premise of this benchmark, which pits NumPyro, affine-invariant MCMC, and dynesty against exact ground truth. The results are a sobering check on our assumptions about modern tools. When you're building AI scaffolding around agents or exploring spatial data pathways, knowing where your inference engine breaks is as important as knowing where it works.
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.