def sample_std_normal(n, seed=1):
np.random.seed(seed)
tuner = mcmc.Tuner(1.0)
xs = np.zeros(n)
x = 0.0
for b in range(n):
x = mcmc.ametropolis(x, dist.Normal(0, 1).lpdf, tuner)
# x = mcmc.ametropolis(x, lpdf_std_normal, tuner) # faster
xs[b] = x
return xs
def update_b2(state, data, tuner_b2):
def log_prob(b2):
return log_prob_b2(b2, state, data)
state.b2 = mcmc.ametropolis(state.b2, log_prob, tuner_b2)
def update_b1(state, data, tuner_b1):
def log_prob(b1):
return log_prob_b1(b1, state, data)
state.b1 = mcmc.ametropolis(state.b1, log_prob, tuner_b1)
def update_b0(state, data, tuner_b0):
log_prob = lambda b0: log_prob_b0(b0, state, data)
state.b0 = mcmc.ametropolis(state.b0, log_prob, tuner_b0)
def update_b2(self):
self.state.b2 = mcmc.ametropolis(self.state.b2, self.logprob_b2,
self.tuners.b2)
def update_b1(self):
self.state.b1 = mcmc.ametropolis(self.state.b1, self.logprob_b1,
self.tuners.b1)
def update_b0(self):
self.state.b0 = mcmc.ametropolis(self.state.b0, self.logprob_b0,
self.tuners.b0)
