Let’s talk about something that sounds intimidating but is actually pretty approachable once you break it down: using Markov Chain Monte Carlo (MCMC) methods for parameter estimation in the kinds of models you’ll see on SOA Exam C. If you’re preparing for the exam, or just curious about how actuaries and statisticians estimate parameters in real-world scenarios, this guide is for you. I’ll walk you through the why, the how, and the what-next, with practical examples and tips from my own experience. By the end, you’ll not only understand MCMC but also feel confident applying it to actuarial models—even if you’re more comfortable with traditional methods like maximum likelihood.