Tag Archives: hit-and-run

I haven’t had time to say much recently, due to some travel for work, but I did have a chance to prototype the Hit-and-Run/Adaptive Metropolis approach to MCMC that I mentioned in my last post.  Was that really three weeks ago?  How time flies.

Anyway, thanks to the tip Aram pointed out in the comments, Hit-and-Run can take steps in the correct direction without explicitly computing an approximation of the covariance matrix, just by taking a randomly weighted sum of random points.  It’s nearly magic, although Aram says that it actually makes plenty of sense. The code for this “H-RAM” looks pretty similar to my original Hit-and-Run step method, and it’s short enough that I’ll just show it to you:

class HRAM(Gibbs):
    def __init__(self, stochastic, proposal_sd=None, verbose=None):
        Metropolis.__init__(self, stochastic, proposal_sd=proposal_sd,
                            verbose=verbose, tally=False)
        self.proposal_tau = self.proposal_sd**-2.
        self.n = 0
        self.N = 11
        self.value = rnormal(self.stochastic.value, self.proposal_tau, size=tuple([self.N] + list(self.stochastic.value.shape)))

    def step(self):
        x0 = self.value[self.n]
        u = rnormal(zeros(self.N), 1.)
        dx = dot(u, self.value)

        self.stochastic.value = x0
        logp = [self.logp_plus_loglike]
        x_prime = [x0]

        for direction in [-1, 1]:
            for i in xrange(25):
                delta = direction*exp(.1*i)*dx
                try:
                    self.stochastic.value = x0 + delta
                    logp.append(self.logp_plus_loglike)
                    x_prime.append(x0 + delta)
                except ZeroProbability:
                    self.stochastic.value = x0

        i = rcategorical(exp(array(logp) - flib.logsum(logp)))
        self.value[self.n] = x_prime[i]
        self.stochastic.value = x_prime[i]

        if i == 0:
            self.rejected += 1
        else:
            self.accepted += 1

        self.n += 1
        if self.n == self.N:
            self.n = 0

Compare the results to the plain old Hit-and-Run step method: