📊 Five Minutes to Understand Modern Statistics
#

This resource brings together key concepts in statistical inference, probabilistic models, and stochastic processes, explained in a direct way with practical examples.
Ideal for people working with data who want to reinforce fundamentals like:

🔍 Bayesian and frequentist inference
📈 Mixture models and the EM algorithm
🔁 Markov chains and Poisson processes
🤖 Sampling methods like MCMC and Gibbs sampling

A compact, well-organized collection that’s perfect to review before teaching, modeling, or just refreshing ideas.

🧩 Explained in a Few Words
#

If you’re new to these topics, think of it like this:

Inference → learning about something unknown using data.
Probabilistic models → mathematical rules that describe how that data could be generated.
Stochastic processes → systems that change randomly over time.
MCMC / Gibbs / EM → techniques to “explore” complex models when we can’t solve them directly.

These are tools that help go from raw data to solid conclusions.