def model():
p_latent = numpyro.sample("p_latent", FakeBeta(alpha0, beta0))
with numpyro.plate("data", len(data)):
numpyro.sample("obs", dist.Bernoulli(p_latent), obs=data)
return p_latent
def guide():
probs = numpyro.param("probs", 0.2)
numpyro.sample("x", dist.Bernoulli(probs))
def model(data):
w = numpyro.sample("w", dist.Bernoulli(0.6))
x = 0
for i, y in markov(enumerate(data)):
x = numpyro.sample(f"x_{i}", dist.Categorical(probs[w, x]))
numpyro.sample(f"y_{i}", dist.Normal(locs[x], 1), obs=y)
def model(data):
alpha = jnp.array([1.1, 1.1])
beta = jnp.array([1.1, 1.1])
p_latent = numpyro.sample("p_latent", dist.Beta(alpha, beta))
numpyro.sample("obs", dist.Bernoulli(p_latent), obs=data)
return p_latent
def model(data, labels):
dim = data.shape[1]
coefs = numpyro.sample('coefs', dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
logits = jnp.dot(data, coefs)
return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
def model():
numpyro.sample("c", dist.Bernoulli(0.8))
def model(data):
x = numpyro.sample("x", dist.Bernoulli(0.5), infer={"enumerate": "parallel"})
with numpyro.plate("N", data.shape[0], subsample_size=100, dim=-1):
batch = numpyro.subsample(data, event_dim=0)
numpyro.sample("obs", dist.Normal(x, 1), obs=batch)
def model(data):
f = sample('beta', dist.Beta(1., 1.))
sample('obs', dist.Bernoulli(f), obs=data)
def model(data):
f = numpyro.sample("beta", dist.Beta(1.0, 1.0))
with numpyro.plate("plate", 10):
numpyro.deterministic("beta_sq", f**2)
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def model(data=None):
with numpyro.plate("dim", 2):
beta = numpyro.sample("beta", dist.Beta(1., 1.))
with numpyro.plate("plate", N, dim=-2):
numpyro.deterministic("beta_sq", beta**2)
numpyro.sample("obs", dist.Bernoulli(beta), obs=data)
def model(labels):
coefs = numpyro.sample('coefs', dist.Normal(np.zeros(dim), np.ones(dim)))
logits = numpyro.deterministic('logits', np.sum(coefs * data, axis=-1))
return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
def model(data):
f = numpyro.sample("beta", dist.Beta(1., 1.))
with numpyro.plate("plate", 10):
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def model(data=None):
beta = numpyro.sample("beta", dist.Beta(np.ones(2), np.ones(2)))
with numpyro.plate("plate", N, dim=-2):
numpyro.sample("obs", dist.Bernoulli(beta), obs=data)
def model(data):
f = numpyro.sample("beta", dist.Beta(1.0, 1.0))
with numpyro.plate("N", len(data)):
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def model():
numpyro.sample("x", dist.Bernoulli(0.7).expand([3]))
numpyro.sample("y", dist.Binomial(10, 0.3))
def model(data):
f = numpyro.sample('beta', dist.Beta(jnp.ones(2), jnp.ones(2)))
numpyro.sample('obs', dist.Bernoulli(f), obs=data)
def model():
numpyro.sample("x", dist.Bernoulli(0.7), infer={"enumerate": "parallel"})
y = numpyro.sample("y", dist.Binomial(10, 0.3))
numpyro.deterministic("y2", y ** 2)
def model(data, labels):
coefs = numpyro.sample('coefs',
dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
logits = jnp.sum(coefs * data, axis=-1)
return numpyro.sample('obs', dist.Bernoulli(logits=logits), obs=labels)
def model():
numpyro.sample("c", dist.Bernoulli(0.8))
numpyro.sample(
"u", dist.ImproperUniform(dist.constraints.unit_interval, (), ())
)
def model(data, labels):
coefs = numpyro.sample("coefs",
dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
offset = numpyro.sample("offset", dist.Uniform(-1, 1))
logits = offset + jnp.sum(coefs * data, axis=-1)
return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=labels)
def discrete(prob):
numpyro.sample("x", dist.Bernoulli(prob))
def model(data):
f = numpyro.sample("beta", dist.Beta(1, 1))
with numpyro.plate("plate", 20, 10, dim=-1):
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def model(labels):
coefs = numpyro.sample("coefs", dist.Normal(jnp.zeros(dim), jnp.ones(dim)))
logits = numpyro.deterministic("logits", jnp.sum(coefs * data, axis=-1))
return numpyro.sample("obs", dist.Bernoulli(logits=logits), obs=labels)
def model(data):
f = numpyro.sample("beta",
dist.Beta(jnp.ones(2), jnp.ones(2)).to_event())
with numpyro.plate("N", N):
numpyro.sample("obs", dist.Bernoulli(f).to_event(1), obs=data)
def model():
numpyro.sample("x", dist.Bernoulli(0.5))
def model(labels):
coefs = sample('coefs', dist.Normal(np.zeros(dim), np.ones(dim)))
logits = np.sum(coefs * data, axis=-1)
return sample('obs', dist.Bernoulli(logits=logits), obs=labels)
def model(data):
f = numpyro.sample("beta", dist.Beta(1.0, 1.0))
numpyro.sample("obs", dist.Bernoulli(f), obs=data)
def model(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
def model(data):
y_prob = numpyro.sample("y_prob", dist.Beta(1., 1.))
with numpyro.plate("data", data.shape[0]):
y = numpyro.sample("y", dist.Bernoulli(y_prob))
z = numpyro.sample("z", dist.Bernoulli(0.65 * y + 0.1))
numpyro.sample("obs", dist.Normal(2. * z, 1.), obs=data)
def model():
c = numpyro.sample("c", dist.Gamma(1, 1))
with handlers.collapse():
probs = numpyro.sample("probs", dist.Beta(c, 2))
numpyro.sample("obs", dist.Bernoulli(probs), obs=data)
