Comments (5)
I guess it just needs a few lines of code. Let me do it. :D
from numpyro.
I tried to use batching.defbroadcasted
, but haven't been able to get it to work. Here's a test case for you 😆
import jax.numpy as np
from jax import random, vmap
from jax.scipy.special import logsumexp
import numpyro.distributions as dist
from numpyro.hmc_util import initialize_model
from numpyro.handlers import sample, seed, substitute, trace
from numpyro.mcmc import mcmc
def beta_bernoulli(data):
alpha = np.array([1.1, 1.1])
beta = np.array([1.1, 1.1])
p_latent = sample('p_latent', dist.Beta(alpha, beta))
sample('obs', dist.Bernoulli(p_latent), obs=data)
return p_latent
num_warmup, num_samples = 500, 1000
true_probs = np.array([0.9, 0.1])
data = dist.Bernoulli(true_probs).sample(random.PRNGKey(1), size=(1000, 2))
init_params, potential_fn, constrain_fn = initialize_model(random.PRNGKey(2), beta_bernoulli, data)
samples = mcmc(num_warmup, num_samples, init_params,
potential_fn=potential_fn,
constrain_fn=constrain_fn)
def log_lk(rng, params, model, *args, **kwargs):
model = substitute(seed(model, rng), params)
model_trace = trace(model).get_trace(*args, **kwargs)
obs_node = model_trace['obs']
return np.sum(obs_node['fn'].log_prob(obs_node['value']))
def expected_log_likelihood(rng, params, model, *args, **kwargs):
n = list(params.values())[0].shape[0]
log_lk_fn = vmap(lambda rng, params: log_lk(rng, params, model, *args, **kwargs))
log_lk_vals = log_lk_fn(random.split(rng, n), params)
return logsumexp(log_lk_vals) - np.log(n)
print(expected_log_likelihood(random.PRNGKey(2), samples, beta_bernoulli, data))
from numpyro.
I thought that it is something like
batching.defvectorized(xlogy.primitive)
from numpyro.
Oppss, it does not work :D
from numpyro.
I think defbroadcasting
will also broadcast the parameters for binary operators if needed, since its used for binop
, but really anything that makes this test case work is fine by me.
from numpyro.
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from numpyro.