Likelihood Estimation
Master Probability, Algorithms, and Real-World Decision Making
Most people think probability is about predicting the future, but likelihood estimation is about something more fundamental: finding the best explanation for what you've already observed. Whether you're building machine learning models, analyzing experimental data, or making algorithmic decisions, likelihood estimation is the mathematical engine behind the scenes. This book cuts through the complexity to show you how likelihood estimation actually works—from foundational concepts to practical implementation. You'll discover why Maximum Likelihood Estimation has become the gold standard in statistics and machine learning, how to navigate the computational challenges of real-world problems, and when to use approximation techniques instead of exact solutions. Written for those with intermediate probability knowledge, this guide combines clear explanations with concrete examples, helping you move from understanding the theory to confidently applying these powerful techniques in your own work.
Est. Length
207 pages (11 chapters)
Language
English
Publication date
Dec 13, 2025
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