Get personalized book recommendations based on your reading history and preferences. Our algorithm analyzes your favorite books and reading patterns to suggest your next great read.

"Maximum likelihood estimation" as a book category encompasses texts focused on the method of estimating model parameters by maximizing the likelihood function; these books range from introductory guides that build intuition and cover basic continuous and discrete likelihoods, log-likelihoods, and simple optimization, to advanced treatments of asymptotic theory (consistency, efficiency, asymptotic normality), Fisher information, likelihood-based hypothesis testing and confidence intervals, and regularity conditions. They typically mix theory, worked examples and applications (econometrics, biostatistics, machine learning, signal processing), and practical computational techniques such as numerical optimization, the EM algorithm, and software implementation. Readers are usually expected to have some background in probability, calculus, and linear algebra, and the books often contrast likelihood approaches with Bayesian methods and discuss model selection, robustness, and diagnostics.

Filter books by their publication year. Enter the earliest year (Start) and latest year (End) to find books published within that period. Leave either field empty to search from the beginning of time or up to the present day.