Book contents
- Frontmatter
- Dedication
- Epigraph
- Contents
- Preface
- Acknowledgements
- Expanded Note for Instructors
- Part I Concepts from Modeling, Inference, and Computing
- 1 Probabilistic Modeling and Inference
- 2 Dynamical Systems and Markov Processes
- 3 Likelihoods and Latent Variables
- 4 Bayesian Inference
- 5 Computational Inference
- Part II Statistical Models
- Part III Appendices
- Index
- Back Cover
3 - Likelihoods and Latent Variables
from Part I - Concepts from Modeling, Inference, and Computing
Published online by Cambridge University Press: 17 August 2023
Book contents
- Frontmatter
- Dedication
- Epigraph
- Contents
- Preface
- Acknowledgements
- Expanded Note for Instructors
- Part I Concepts from Modeling, Inference, and Computing
- 1 Probabilistic Modeling and Inference
- 2 Dynamical Systems and Markov Processes
- 3 Likelihoods and Latent Variables
- 4 Bayesian Inference
- 5 Computational Inference
- Part II Statistical Models
- Part III Appendices
- Index
- Back Cover
Summary
In this chapter we introduce the concept of likelihoods, how to incorporate measurement uncertainty into likelihoods, and the concept of latent variables that arise in describing measurements. We then show how the principle of maximum likelihood is applied to estimate values for unknown parameters and discuss a number of numerical optimization techniques used in obtaining maximum likelihood estimators. These techniques include a discussion of expectation maximization.
- Type
- Chapter
- Information
- Data Modeling for the SciencesApplications, Basics, Computations, pp. 108 - 130Publisher: Cambridge University PressPrint publication year: 2023