Information Geometry and Population Genetics: The by Julian Hofrichter, Jürgen Jost, Tat Dat Tran

By Julian Hofrichter, Jürgen Jost, Tat Dat Tran

The current monograph develops a flexible and profound mathematical standpoint of the Wright--Fisher version of inhabitants genetics. This famous and intensively studied version includes a wealthy and lovely mathematical constitution, that is exposed right here in a scientific demeanour. as well as techniques by way of research, combinatorics and PDE, a geometrical point of view is introduced in via Amari's and Chentsov's info geometry. this idea permits us to calculate many amounts of curiosity systematically; likewise, the hired worldwide viewpoint elucidates the stratification of the version in an unparalleled demeanour. additionally, the hyperlinks to statistical mechanics and big deviation concept are explored and constructed into robust instruments. Altogether, the manuscript presents an outstanding and large operating foundation for graduate scholars and researchers attracted to this field.

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Extra resources for Information Geometry and Population Genetics: The Mathematical Structure of the Wright-Fisher Model

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X/ @x@ i . TM/. 6) is called contravariant. 6). The basic such k 48 3 Geometric Structures and Information Geometry objects are the covectors ! i dxi . 7) we can apply a covector ! V/. V/. We can then also form other tensors, with more than one index. A lower index always indicates covariant, an upper one contravariant˝transformation. For example, ˛ the metric tensor, written as gij dxi ˝ dxj , with gij D @x@ i ; @x@ j being the product of those two basis vectors, operates on pairs of tangent vectors.

0 ; : : : ; ˛ n / is a multiindex with n P C 1 (the number of alleles nonnegative integer entries. We also put Q present) i j˛j D ˛ i . X 0 ; : : : ; X n /. 1 Notation for Random Variables We start with some notations. We may have either integer times m 2 N or real times t 2 RC . Time is represented by a subscript. So, Zm or Zt is the value of the random process Z at time m or t. We usually consider a population of N diploid individuals. Thus, in the single locus case, when every individual carries two alleles, we have a population of 2N alleles.

5) 20 2 The Wright–Fisher Model that is, we lump the alleles Aij 1 C1 ; : : : ; Aij into the single super-allele Bj . 3 The Basic Wright–Fisher Model For the Wright–Fisher model, we simply iterate this process across several generations. Thus, we introduce a discrete time m and let this time m now be the subscript for Y instead of the 2N that we had employed so far to indicate the total number of alleles present in the population. Instead of the absolute probabilities of multinomial sampling, we now need to consider the transition probabilities.

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