1.

Record Nr.

UNINA9910254291003321

Autore

Hofrichter Julian

Titolo

Information Geometry and Population Genetics : The Mathematical Structure of the Wright-Fisher Model / / by Julian Hofrichter, Jürgen Jost, Tat Dat Tran

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017

ISBN

3-319-52045-8

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (XII, 320 p. 3 illus., 2 illus. in color.)

Collana

Understanding Complex Systems, , 1860-0840

Disciplina

576.58015118

Soggetti

Biomathematics

Statistics

Medical genetics

Mathematical analysis

Geometry

Probabilities

Mathematical and Computational Biology

Statistical Theory and Methods

Medical Genetics

Analysis

Probability Theory

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

1. Introduction -- 2. The Wright–Fisher model -- 3. Geometric structures and information geometry -- 4. Continuous approximations -- 5. Recombination -- 6. Moment generating and free energy functionals -- 7. Large deviation theory -- 8. The forward equation -- 9. The backward equation -- 10.Applications -- Appendix -- A. Hypergeometric functions and their generalizations -- Bibliography.

Sommario/riassunto

The present monograph develops a versatile and profound mathematical perspective of the Wright--Fisher model of population genetics. This well-known and intensively studied model carries a rich and beautiful mathematical structure, which is uncovered here in a



systematic manner. In addition to approaches by means of analysis, combinatorics and PDE, a geometric perspective is brought in through Amari's and Chentsov's information geometry. This concept allows us to calculate many quantities of interest systematically; likewise, the employed global perspective elucidates the stratification of the model in an unprecedented manner. Furthermore, the links to statistical mechanics and large deviation theory are explored and developed into powerful tools. Altogether, the manuscript provides a solid and broad working basis for graduate students and researchers interested in this field.