def rvs(theta):
n1.mean = theta
k = n1.rvs()
k[0] = np.abs(k[0])
return k
n2 = normal(np.zeros(blr.n), 3 * np.eye(blr.n))
def pdf(x, y):
n2.mean = x
return n2.pdf(y)
prop = ProposalDistribution(pdf, rvs)
mcmc = Mcmc(prop, blr)
first_sample = blr.generate_first_sample()
samples = mcmc.sample(10000, first_sample)
utils.plot(xs, ys, samples, blr.n)
ss, hs, sigmas = utils.individual_samples(samples, blr.n)
smeans = np.mean(ss, 1)
hmeans = np.mean(hs, 1)
n3 = normal(0, 2)
def rvs(x):
n3.mean = x
return [n3.rvs()]
def pdf(x, y):
n3.mean = x
return n3.pdf(y)
stationary = ProposalDistribution(stat, stat)
proposal = ProposalDistribution(pdf, rvs)
normative, err = quad(stat, -10, 10)
mcmc = Mcmc(proposal, stationary)
samples = mcmc.sample(10000, [0])
samples = [sample[0] for sample in samples]
xs = np.linspace(-10, 10, 1000)
ys = [stat(x) / normative for x in xs]
plt.plot(xs, ys, 'r')
plt.hist(samples, normed=True, bins=100)
plt.savefig('images/mcmc_2normals.png')
plt.gcf().clear()