Small Markov chain Monte Carlo implementation

I've written a small markov chain monte carlo function that takes samples from a posterior distribution, based on a prior and a binomial (Bin(N, Z)) distribution.

I'd be happy to have it reviewed, especially perhaps, regarding how to properly pass functions as arguments to functions (as the function prior_dist() in my code). In this case, I'm passing the function uniform_prior_distribution() showed below, but it's quite likely I'd like to pass other functions, that accept slightly different arguments, in the future. This would require me to rewrite mcmc(), unless there's some smart way around it...

def mcmc(prior_dist, size=100000, burn=1000, thin=10, Z=3, N=10):
    import random
    from scipy.stats import binom
    #Make Markov chain (Monte Carlo)
    mc = [0] #Initialize markov chain
    while len(mc)  random.random(): #Acceptence criteria
        mc.append(cand)
    else:
        mc.append(mc[-1])
    #Take sample
    sample = []
    for i in range(len(mc)):
        if i >= burn and (i-burn)%thin == 0:
            sample.append(mc[i])
    sample = sorted(sample)
    #Estimate posterior probability
    post = []
    for p in sample:
        post.append(binom.pmf(Z, N, p) * prior_dist(p, size))
    return sample, post, mc

def uniform_prior_distribution(p, size):
    prior = 1.0/size
    return prior

def mcmc(prior_dist, size=100000, burn=1000, thin=10, Z=3, N=10):
    import random
    from scipy.stats import binom

Don't import inside functions. It goes against python convention and is inefficient

#Make Markov chain (Monte Carlo)
    mc = [0] #Initialize markov chain
    while len(mc)  random.random(): #Acceptence criteria
            mc.append(cand)
    else:
        mc.append(mc[-1])

Why is this in else clause? It only make sense to use else clauses if you are using break.

#Take sample
    sample = []
    for i in range(len(mc)):
        if i >= burn and (i-burn)%thin == 0:
            sample.append(mc[i])

Actually something like sample = mc[burn::thin] will have the same effect as this loop

sample = sorted(sample)
    #Estimate posterior probability
    post = []
    for p in sample:
        post.append(binom.pmf(Z, N, p) * prior_dist(p, size))

I'd suggest post = [binom.pmf(Z, N, p) * prior_dist(p, size) for p in sample]

return sample, post, mc

def uniform_prior_distribution(p, size):
    prior = 1.0/size
    return prior

StackExchange Code Review Q#10851, answer score: 2