def __init__(self, concentration1, concentration0, total_count=1, validate_args=None):
self.concentration1, self.concentration0, self.total_count = promote_shapes(
concentration1, concentration0, total_count
)
batch_shape = lax.broadcast_shapes(jnp.shape(concentration1), jnp.shape(concentration0),
jnp.shape(total_count))
concentration1 = jnp.broadcast_to(concentration1, batch_shape)
concentration0 = jnp.broadcast_to(concentration0, batch_shape)
self._beta = Beta(concentration1, concentration0)
super(BetaBinomial, self).__init__(batch_shape, validate_args=validate_args)
class BetaBinomial(Distribution):
r"""
Compound distribution comprising of a beta-binomial pair. The probability of
success (``probs`` for the :class:`~numpyro.distributions.Binomial` distribution)
is unknown and randomly drawn from a :class:`~numpyro.distributions.Beta` distribution
prior to a certain number of Bernoulli trials given by ``total_count``.
:param numpy.ndarray concentration1: 1st concentration parameter (alpha) for the
Beta distribution.
:param numpy.ndarray concentration0: 2nd concentration parameter (beta) for the
Beta distribution.
:param numpy.ndarray total_count: number of Bernoulli trials.
"""
arg_constraints = {'concentration1': constraints.positive, 'concentration0': constraints.positive,
'total_count': constraints.nonnegative_integer}
has_enumerate_support = True
is_discrete = True
enumerate_support = BinomialProbs.enumerate_support
def __init__(self, concentration1, concentration0, total_count=1, validate_args=None):
self.concentration1, self.concentration0, self.total_count = promote_shapes(
concentration1, concentration0, total_count
)
batch_shape = lax.broadcast_shapes(jnp.shape(concentration1), jnp.shape(concentration0),
jnp.shape(total_count))
concentration1 = jnp.broadcast_to(concentration1, batch_shape)
concentration0 = jnp.broadcast_to(concentration0, batch_shape)
self._beta = Beta(concentration1, concentration0)
super(BetaBinomial, self).__init__(batch_shape, validate_args=validate_args)
def sample(self, key, sample_shape=()):
assert is_prng_key(key)
key_beta, key_binom = random.split(key)
probs = self._beta.sample(key_beta, sample_shape)
return BinomialProbs(total_count=self.total_count, probs=probs).sample(key_binom)
@validate_sample
def log_prob(self, value):
return (-_log_beta_1(self.total_count - value + 1, value) +
betaln(value + self.concentration1, self.total_count - value + self.concentration0) -
betaln(self.concentration0, self.concentration1))
@property
def mean(self):
return self._beta.mean * self.total_count
@property
def variance(self):
return self._beta.variance * self.total_count * (self.concentration0 + self.concentration1 + self.total_count)
@property
def support(self):
return constraints.integer_interval(0, self.total_count)
class BetaBinomial(Distribution):
r"""
Compound distribution comprising of a beta-binomial pair. The probability of
success (``probs`` for the :class:`~numpyro.distributions.Binomial` distribution)
is unknown and randomly drawn from a :class:`~numpyro.distributions.Beta` distribution
prior to a certain number of Bernoulli trials given by ``total_count``.
:param numpy.ndarray concentration1: 1st concentration parameter (alpha) for the
Beta distribution.
:param numpy.ndarray concentration0: 2nd concentration parameter (beta) for the
Beta distribution.
:param numpy.ndarray total_count: number of Bernoulli trials.
"""
arg_constraints = {
'concentration1': constraints.positive,
'concentration0': constraints.positive,
'total_count': constraints.nonnegative_integer
}
has_enumerate_support = True
is_discrete = True
def __init__(self,
concentration1,
concentration0,
total_count=1,
validate_args=None):
batch_shape = lax.broadcast_shapes(np.shape(concentration1),
np.shape(concentration0),
np.shape(total_count))
self.concentration1 = np.broadcast_to(concentration1, batch_shape)
self.concentration0 = np.broadcast_to(concentration0, batch_shape)
self.total_count, = promote_shapes(total_count, shape=batch_shape)
self._beta = Beta(self.concentration1, self.concentration0)
super(BetaBinomial, self).__init__(batch_shape,
validate_args=validate_args)
def sample(self, key, sample_shape=()):
key_beta, key_binom = random.split(key)
probs = self._beta.sample(key_beta, sample_shape)
return Binomial(self.total_count, probs).sample(key_binom)
@validate_sample
def log_prob(self, value):
log_factorial_n = gammaln(self.total_count + 1)
log_factorial_k = gammaln(value + 1)
log_factorial_nmk = gammaln(self.total_count - value + 1)
return (log_factorial_n - log_factorial_k - log_factorial_nmk +
betaln(value + self.concentration1,
self.total_count - value + self.concentration0) -
betaln(self.concentration0, self.concentration1))
@property
def mean(self):
return self._beta.mean * self.total_count
@property
def variance(self):
return self._beta.variance * self.total_count * (
self.concentration0 + self.concentration1 + self.total_count)
@property
def support(self):
return constraints.integer_interval(0, self.total_count)
def enumerate_support(self, expand=True):
total_count = np.amax(self.total_count)
if not_jax_tracer(total_count):
# NB: the error can't be raised if inhomogeneous issue happens when tracing
if np.amin(self.total_count) != total_count:
raise NotImplementedError(
"Inhomogeneous total count not supported"
" by `enumerate_support`.")
values = np.arange(total_count +
1).reshape((-1, ) + (1, ) * len(self.batch_shape))
if expand:
values = np.broadcast_to(values,
values.shape[:1] + self.batch_shape)
return values