Maximum A Posteriori (MAP)

Explore the concept of Maximum A Posteriori (MAP) estimation within convex optimization. Understand how MAP incorporates prior knowledge to improve parameter estimates, avoid overfitting common with Maximum Likelihood Estimation, and apply these techniques to real-world problems such as the Bernoulli distribution. Gain practical skills computing MAP estimates using gradient methods and Bayesian inference.