# markov-realization: Realizations of Markov chains.

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Versions [RSS] [faq] 0.1.0, 0.2.1, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.4 (info) ChangeLog.md base (>=4.7 && <5), contravariant (>=1.5.1 && <1.6), discrimination (==0.4.*), generic-deriving (>=1.12.4 && <1.13), MonadRandom (>=0.5.1.1 && <0.6) [details] BSD-3-Clause 2019 Alex Loomis Alex Loomis atloomis@math.arizona.edu Statistics https://github.com/alexloomis/markov https://github.com/alexloomis/markov/issues head: git clone git://github.com/alexloomis/markov.git by alexloomis at 2019-06-17T00:25:19Z NixOS:0.4 1803 total (6 in the last 30 days) (no votes yet) [estimated by Bayesian average] λ λ λ Docs available Last success reported on 2019-06-17

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# Markov Tutorial

Let Xn denote the nth state of a Markov chain with state space ℕ. For x ≠ 0 define transition probabilities

p(x,0) = q,

p(x,x) = r, and

p(x,x+1) = s.

When x = 0, let p(x,0) = q+r, p(x,x+1) = s. Let p(x,y) = 0 in all other cases. Suppose we wanted to find P[Xn = j ∩ d = k], where d denotes the number of transitions from a positive integer to zero. There are three values we need to track — extinctions, probability, and state. Extinctions add a value to a counter each time they happen and the counter takes integral values, so they can be represented by Sum Int. Probabilities are multiplied each step, and added when duplicate steps are combined. We want decimal probabilities, so we can represent this with Product Rational. We will make a new type for the state.

data Extinction = Extinction Int
deriving Generic
deriving newtype (Eq, Num, Show)
deriving anyclass Grouping


All that remains is to make an instance of Markov.

instance Markov (Sum Int, Product Rational) Extinction where
transition x = case state x of
0 -> [ 0 >*< (q+r) >*< id
, 0 >*< s >*< (+1) ]
_ -> [ 1 >*< q >*< const 0
, 0 >*< r >*< id
, 0 >*< s >*< (+1) ]
where q = 0.1; r = 0.3; s = 0.6


We can now easily see a list of states, deaths, and the probabilities.


> chain [pure 0 :: Sum Int :* Product Rational :* Extinction] !! 3

((0,8 % 125),0)
((0,111 % 500),1)
((1,51 % 500),0)
((0,9 % 25),2)
((1,9 % 250),1)
((0,27 % 125),3


This means that starting from a state of zero, after three time steps there is a 51/500 chance that the state is zero and there has been one death.