Algebraic statistics uses tools from algebraic geometry, commutative algebra, and combinatorics to address problems in statistics and its applications. One of the main guiding principles is that statistical models are semialgebraic sets. From this observation, the geometriy and algebra of the underlying statistical models can be used to get a better understanding of statistical models, analyze statistical procedures, and devise new methods for analyzing data. This book provides an introduction to this subject area, suitable for graduate courses, with background material on probability, algebra, and statistics. My guiding principle in writing the book has been to try to introduce statistical concepts and the mathematical concepts that go with them at the same time, wherever possible. I have now submitted this draft to the publisher. I would still appreciate further suggestions or comments if you have them. |

Algebraic Statistics (Version of January 25, 2018)

List of Chapters

- Introduction
- Probability Primer
- Algebra Primer
- Conditional Independence
- Statistics Primer
- Exponential Families
- Likelihood Inference
- The Cone of Sufficient Statistics
- Fisher's Exact Test
- Bounds on Cell Entries
- Exponential Random Graph Models
- Design of Experiments
- Graphical Models
- Hidden Variables
- Phylogenetic Models
- Identifiability
- Model Selection
- MAP Estimation
- Finite Metric Spaces