Wright-Fisher model
Suppose that in the population there exists two types of genes A and a, then we denote Xn the number of A in the population, whose size is always N. Then the evolution is a Markov chain and the transition matrix is a binomial type
P(XNn+1=k|XNn=i)=CkN(iN)k(1−iN)N−i
I
It is easy to check that XNn is a martingale and its L2 norm is bounded and the Markov chain is positive recurrent, so
XNnn→∞→a.sXN∞∈{0,N}
Using the theorem of stopping time we get that P(XN∞=N)=iN where i is the initial state.
P(XNn+1=k|XNn=i)=CkN(iN)k(1−iN)N−i
I
It is easy to check that XNn is a martingale and its L2 norm is bounded and the Markov chain is positive recurrent, so
XNnn→∞→a.sXN∞∈{0,N}
Using the theorem of stopping time we get that P(XN∞=N)=iN where i is the initial state.
Some other version can also be developped like the model with mutation and selection. On another hand, if we change the scale of time like Zt=1NXN[Nt], the convergence of trajectoire implies that
Zt=Z0+∫t0√Zs(1−Zs)dBs
which relates a discrete model with a continuous random process.
Kingman coalescence model
The state is defined on the partition of [1,N] and the initial state is X0={1}{2}⋯{N}. We note Ti the i-th jump time which follows the law Exp((N−i)(N−i−1)2) and choose uniformly two block to make one block. Some obvious properties are observed.
1. Each time, the number of block minus one.
2. The expectation of fusion time is ∑N−1i=12(N−i)(N−i−1)=2(1−1N−1) and it converges.
3. We define the genetic relation π′→π if the later is the generated by fusing two blocks of the former. The transition matrix is
P(π′,π)=2♯π′⋅(♯π′−1)Iπ′→π
However, the most beautiful formule is
PTN−k(π)=k!N!(N−k)!(k−1)!(N−1)!l∏i=1♯Bi
for π=⨆ki=1{Bi}
The proof is just a recurrence but the structure of formula is really beautiful, isn't?
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