Metropolis-Hasting Algorithm 2 Markov Chain

exercise.py

# coding: utf-8
#r = 8

# p is a probability constructed in the following way:
# node position:   0-0  0-1 0-2  1-0 1-1 1-2 2-0  2-1 2-2
# probability:     1/16 1/8 1/16 1/8 1/4 1/8 1/16 1/8 1/16
# so we have 8 nodes here, and the output will be a way such that the probability is stationary.

p = [0.0625    ,0.1250    ,0.0625    ,0.1250    ,0.2500    ,0.1250,0.0625    ,0.1250   ,0.0625]
#p = [0.5, 0.33333333, 0.166666667]
N = len(p)
r = N-1

def prob(i, j):
	if i != j:
		temp = float(1)/r
		#print temp
		joi = p[j]/p[i]
		#print ioj
		temp = temp * min(1,joi)
		return temp
	else:
		temp = 0
		for j in range(N):
			if i != j:
				temp += prob(i, j)
		return 1 - temp

# print prob(1, 1)
templist = []
for i in range(N):
	nlist = []
	for j in range(N):
		nlist.append(prob(i, j))
		#print "Prob(" + str(i) + "->" + str(j) + ") is " + str(prob(i, j))
	print nlist
	if sum(nlist) != 1:
		print "WARNING: Sum is not 1"