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Likelihood-Based Inference: From Foundations to Research
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Likelihood-Based Inference: From Foundations to Research

Contents

  • Part I: Foundations
    • Chapter 1: Probability Basics
    • Chapter 2: Random Variables
    • Chapter 3: Common Distributions
    • Chapter 4: The Likelihood Function
  • Part II: The Likelihood Catalogue
    • Chapter 5 — Discrete Likelihoods
    • Chapter 6 — Continuous Likelihoods
    • Chapter 7 — Multivariate Likelihoods
    • Chapter 8 — Specialized Likelihoods
  • Part III: Maximum Likelihood Estimation
    • Chapter 9 — MLE Theory
    • Chapter 10 — Analytical MLE Solutions
    • Chapter 11 — Confidence Intervals and Hypothesis Testing
  • Part IV: Optimization for Likelihood
    • Chapter 12: Gradient Methods
    • Chapter 13: Newton and Scoring Methods
    • Chapter 14: Quasi-Newton Methods
    • Chapter 15: The EM Algorithm
    • Chapter 16: Constrained Optimization
  • Part V: Advanced and Research Topics
    • Chapter 17 – Bayesian Connections
    • Chapter 18 – Computational Methods
    • Chapter 19 – Model Selection
    • Chapter 20 – Modern Research Frontiers
    • Chapter 21 – Numerical Considerations
  • Appendices
    • Appendix A: Linear Algebra Review
    • Appendix B: Calculus Review
    • Appendix C: Notation and Glossary
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