tfp.distributions.MultivariateStudentTLinearOperator

Class `MultivariateStudentTLinearOperator`

The [Multivariate Student's t-distribution](

Inherits From: Distribution

Defined in python/distributions/multivariate_student_t.py.

https://en.wikipedia.org/wiki/Multivariate_t-distribution) on R^k.

Mathematical Details

The probability density function (pdf) is,

pdf(x; df, loc, Sigma) = (1 + ||y||**2 / df)**(-0.5 (df + k)) / Z
where,
y = inv(Sigma) (x - loc)
Z = abs(det(Sigma)) sqrt(df pi)**k Gamma(0.5 df) / Gamma(0.5 (df + k))

where:

df is a positive scalar.
loc is a vector in R^k,
Sigma is a positive definite shape' matrix inR^{k x k}, parameterized asscale @ scale.T` in this class,
Z denotes the normalization constant, and,
||y||**2 denotes the squared Euclidean norm of y.

The Multivariate Student's t-distribution distribution is a member of the location-scale family, i.e., it can be constructed as,

X ~ MultivariateT(loc=0, scale=1)   # Identity scale, zero shift.
Y = scale @ X + loc

Examples

tfd = tfp.distributions

# Initialize a single 3-variate Student's t.
df = 3.
loc = [1., 2, 3]
scale = [[ 0.6,  0. ,  0. ],
         [ 0.2,  0.5,  0. ],
         [ 0.1, -0.3,  0.4]]
sigma = tf.matmul(scale, scale, adjoint_b=True)
# ==> [[ 0.36,  0.12,  0.06],
#      [ 0.12,  0.29, -0.13],
#      [ 0.06, -0.13,  0.26]]

mvt = tfd.MultivariateStudentTLinearOperator(
    df=df,
    loc=loc,
    scale=tf.linalg.LinearOperatorLowerTriangular(scale))

# Covariance is closely related to the sigma matrix (for df=3, it is 3x of the
# sigma matrix).

mvt.covariance().eval()
# ==> [[ 1.08,  0.36,  0.18],
#      [ 0.36,  0.87, -0.39],
#      [ 0.18, -0.39,  0.78]]

# Compute the pdf of an`R^3` observation; return a scalar.
mvt.prob([-1., 0, 1]).eval()  # shape: []

<h2 id="__init__"><code>__init__</code></h2>

``` python
__init__(
    df,
    loc,
    scale,
    validate_args=False,
    allow_nan_stats=True,
    name='MultivariateStudentTLinearOperator'
)

Construct Multivariate Student's t-distribution on R^k.

The batch_shape is the broadcast shape between df, loc and scale arguments.

The event_shape is given by last dimension of the matrix implied by scale. The last dimension of loc must broadcast with this.

Additional leading dimensions (if any) will index batches.

Args:

df: A positive floating-point Tensor. Has shape [B1, ..., Bb] where b >= 0.
loc: Floating-point Tensor. Has shape [B1, ..., Bb, k] where k is the event size.
scale: Instance of LinearOperator with a floating dtype and shape [B1, ..., Bb, k, k].
validate_args: Python bool, default False. Whether to validate input with asserts. If validate_args is False, and the inputs are invalid, correct behavior is not guaranteed.
allow_nan_stats: Python bool, default True. If False, raise an exception if a statistic (e.g. mean/variance/etc...) is undefined for any batch member If True, batch members with valid parameters leading to undefined statistics will return NaN for this statistic.
name: The name to give Ops created by the initializer.

Raises:

TypeError: if not scale.dtype.is_floating.
ValueError: if not scale.is_positive_definite.

Properties

`allow_nan_stats`

Python bool describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined.

Returns:

allow_nan_stats: Python bool.

`batch_shape`

Shape of a single sample from a single event index as a TensorShape.

May be partially defined or unknown.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Returns:

batch_shape: TensorShape, possibly unknown.

`df`

The degrees of freedom of the distribution.

This controls the degrees of freedom of the distribution. The tails of the distribution get more heavier the smaller df is. As df goes to infinitiy, the distribution approaches the Multivariate Normal with the same loc and scale.

Returns:

The df Tensor.

`dtype`

The DType of Tensors handled by this Distribution.

`event_shape`

Shape of a single sample from a single batch as a TensorShape.

May be partially defined or unknown.

Returns:

event_shape: TensorShape, possibly unknown.

`loc`

The location parameter of the distribution.

loc applies an elementwise shift to the distribution.

X ~ MultivariateT(loc=0, scale=1)   # Identity scale, zero shift.
Y = scale @ X + loc

Returns:

The loc Tensor.

`name`

Name prepended to all ops created by this Distribution.

`parameters`

Dictionary of parameters used to instantiate this Distribution.

`reparameterization_type`

Describes how samples from the distribution are reparameterized.

Currently this is one of the static instances tfd.FULLY_REPARAMETERIZED or tfd.NOT_REPARAMETERIZED.

Returns:

An instance of ReparameterizationType.

`scale`

The scale parameter of the distribution.

scale applies an affine scale to the distribution.

X ~ MultivariateT(loc=0, scale=1)   # Identity scale, zero shift.
Y = scale @ X + loc

Returns:

The scale LinearOperator.

`validate_args`

Python bool indicating possibly expensive checks are enabled.

Methods

`getitem`

__getitem__(slices)

Slices the batch axes of this distribution, returning a new instance.

b = tfd.Bernoulli(logits=tf.zeros([3, 5, 7, 9]))
b.batch_shape  # => [3, 5, 7, 9]
b2 = b[:, tf.newaxis, ..., -2:, 1::2]
b2.batch_shape  # => [3, 1, 5, 2, 4]

x = tf.random.normal([5, 3, 2, 2])
cov = tf.matmul(x, x, transpose_b=True)
chol = tf.cholesky(cov)
loc = tf.random.normal([4, 1, 3, 1])
mvn = tfd.MultivariateNormalTriL(loc, chol)
mvn.batch_shape  # => [4, 5, 3]
mvn.event_shape  # => [2]
mvn2 = mvn[:, 3:, ..., ::-1, tf.newaxis]
mvn2.batch_shape  # => [4, 2, 3, 1]
mvn2.event_shape  # => [2]

Args:

slices: slices from the [] operator

Returns:

dist: A new tfd.Distribution instance with sliced parameters.

`iter`

__iter__()

`batch_shape_tensor`

batch_shape_tensor(name='batch_shape_tensor')

Shape of a single sample from a single event index as a 1-D Tensor.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Args:

name: name to give to the op

Returns:

batch_shape: Tensor.

`cdf`

cdf(
    value,
    name='cdf',
    **kwargs
)

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`copy`

copy(**override_parameters_kwargs)

Creates a deep copy of the distribution.

Args:

**override_parameters_kwargs: String/value dictionary of initialization arguments to override with new values.

Returns:

distribution: A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

`covariance`

covariance(
    name='covariance',
    **kwargs
)

Covariance.

Covariance is (possibly) defined only for non-scalar-event distributions.

For example, for a length-k, vector-valued distribution, it is calculated as,

Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]

where Cov is a (batch of) k x k matrix, 0 <= (i, j) < k, and E denotes expectation.

Alternatively, for non-vector, multivariate distributions (e.g., matrix-valued, Wishart), Covariance shall return a (batch of) matrices under some vectorization of the events, i.e.,

Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]

where Cov is a (batch of) k' x k' matrices, 0 <= (i, j) < k' = reduce_prod(event_shape), and Vec is some function mapping indices of this distribution's event dimensions to indices of a length-k' vector.

Additional documentation from MultivariateStudentTLinearOperator:

The covariance for Multivariate Student's t equals

scale @ scale.T * df / (df - 2), when df > 2
infinity, when 1 < df <= 2
NaN, when df <= 1

If self.allow_nan_stats=False, then an exception will be raised rather than returning NaN.

Args:

name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

covariance: Floating-point Tensor with shape [B1, ..., Bn, k', k'] where the first n dimensions are batch coordinates and k' = reduce_prod(self.event_shape).

`cross_entropy`

cross_entropy(
    other,
    name='cross_entropy'
)

Computes the (Shannon) cross entropy.

Denote this distribution (self) by P and the other distribution by Q. Assuming P, Q are absolutely continuous with respect to one another and permit densities p(x) dr(x) and q(x) dr(x), (Shannon) cross entropy is defined as:

H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x)

where F denotes the support of the random variable X ~ P.

Args:

other: tfp.distributions.Distribution instance.
name: Python str prepended to names of ops created by this function.

Returns:

cross_entropy: self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of (Shannon) cross entropy.

`entropy`

entropy(
    name='entropy',
    **kwargs
)

Shannon entropy in nats.

`event_shape_tensor`

event_shape_tensor(name='event_shape_tensor')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:

name: name to give to the op

Returns:

event_shape: Tensor.

`is_scalar_batch`

is_scalar_batch(name='is_scalar_batch')

Indicates that batch_shape == [].

Args:

name: Python str prepended to names of ops created by this function.

Returns:

is_scalar_batch: bool scalar Tensor.

`is_scalar_event`

is_scalar_event(name='is_scalar_event')

Indicates that event_shape == [].

Args:

name: Python str prepended to names of ops created by this function.

Returns:

is_scalar_event: bool scalar Tensor.

`kl_divergence`

kl_divergence(
    other,
    name='kl_divergence'
)

Computes the Kullback--Leibler divergence.

Denote this distribution (self) by p and the other distribution by q. Assuming p, q are absolutely continuous with respect to reference measure r, the KL divergence is defined as:

KL[p, q] = E_p[log(p(X)/q(X))]
         = -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x)
         = H[p, q] - H[p]

where F denotes the support of the random variable X ~ p, H[., .] denotes (Shannon) cross entropy, and H[.] denotes (Shannon) entropy.

Args:

other: tfp.distributions.Distribution instance.
name: Python str prepended to names of ops created by this function.

Returns:

kl_divergence: self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of the Kullback-Leibler divergence.

`log_cdf`

log_cdf(
    value,
    name='log_cdf',
    **kwargs
)

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`log_prob`

log_prob(
    value,
    name='log_prob',
    **kwargs
)

Log probability density/mass function.

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`log_survival_function`

log_survival_function(
    value,
    name='log_survival_function',
    **kwargs
)

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`mean`

mean(
    name='mean',
    **kwargs
)

Mean.

Additional documentation from MultivariateStudentTLinearOperator:

The mean of Student's T equals loc if df > 1, otherwise it is NaN. If self.allow_nan_stats=False, then an exception will be raised rather than returning NaN.

`mode`

mode(
    name='mode',
    **kwargs
)

Mode.

`param_shapes`

param_shapes(
    cls,
    sample_shape,
    name='DistributionParamShapes'
)

Shapes of parameters given the desired shape of a call to sample().

This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample().

Subclasses should override class method _param_shapes.

Args:

sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
name: name to prepend ops with.

Returns:

dict of parameter name to Tensor shapes.

`param_static_shapes`

param_static_shapes(
    cls,
    sample_shape
)

param_shapes with static (i.e. TensorShape) shapes.

This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Assumes that the sample's shape is known statically.

Subclasses should override class method _param_shapes to return constant-valued tensors when constant values are fed.

Args:

sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().

Returns:

dict of parameter name to TensorShape.

Raises:

ValueError: if sample_shape is a TensorShape and is not fully defined.

`prob`

prob(
    value,
    name='prob',
    **kwargs
)

Probability density/mass function.

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`quantile`

quantile(
    value,
    name='quantile',
    **kwargs
)

Quantile function. Aka "inverse cdf" or "percent point function".

Given random variable X and p in [0, 1], the quantile is:

quantile(p) := x such that P[X <= x] == p

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

quantile: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`sample`

sample(
    sample_shape=(),
    seed=None,
    name='sample',
    **kwargs
)

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:

sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
seed: Python integer seed for RNG
name: name to give to the op.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

samples: a Tensor with prepended dimensions sample_shape.

`stddev`

stddev(
    name='stddev',
    **kwargs
)

Standard deviation.

Standard deviation is defined as,

stddev = E[(X - E[X])**2]**0.5

where X is the random variable associated with this distribution, E denotes expectation, and stddev.shape = batch_shape + event_shape.

Additional documentation from MultivariateStudentTLinearOperator:

The standard deviation for Student's T equals

sqrt(diag(scale @ scale.T)) * df / (df - 2), when df > 2
infinity, when 1 < df <= 2
NaN, when df <= 1

Args:

name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

stddev: Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().

`survival_function`

survival_function(
    value,
    name='survival_function',
    **kwargs
)

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).

Args:

value: float or double Tensor.
name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

`variance`

variance(
    name='variance',
    **kwargs
)

Variance.

Variance is defined as,

Var = E[(X - E[X])**2]

where X is the random variable associated with this distribution, E denotes expectation, and Var.shape = batch_shape + event_shape.

Additional documentation from MultivariateStudentTLinearOperator:

The variance for Student's T equals

diag(scale @ scale.T) * df / (df - 2), when df > 2
infinity, when 1 < df <= 2
NaN, when df <= 1

If self.allow_nan_stats=False, then an exception will be raised rather than returning NaN.

Args:

name: Python str prepended to names of ops created by this function.
**kwargs: Named arguments forwarded to subclass implementation.

Returns:

variance: Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().

tfp.distributions.MultivariateStudentTLinearOperator

Class MultivariateStudentTLinearOperator

Mathematical Details

Examples

Args:

Raises:

Properties

allow_nan_stats

Returns:

batch_shape

Returns:

df

Returns:

dtype

event_shape

Returns:

loc

Returns:

name

parameters

reparameterization_type

Returns:

scale

Returns:

validate_args

Methods

__getitem__

Args:

Returns:

__iter__

batch_shape_tensor

Args:

Returns:

cdf

Args:

Returns:

copy

Args:

Returns:

covariance

Args:

Returns:

cross_entropy

Args:

Returns:

entropy

event_shape_tensor

Args:

Returns:

is_scalar_batch

Args:

Returns:

is_scalar_event

Args:

Returns:

kl_divergence

Args:

Returns:

log_cdf

Args:

Returns:

log_prob

Args:

Returns:

log_survival_function

Args:

Returns:

mean

mode

param_shapes

Args:

Returns:

param_static_shapes

Args:

Returns:

Raises:

prob

Args:

Returns:

quantile

Class `MultivariateStudentTLinearOperator`

`allow_nan_stats`

`batch_shape`

`df`

`dtype`

`event_shape`

`loc`

`name`

`parameters`

`reparameterization_type`

`scale`

`validate_args`

`getitem`

`iter`

`batch_shape_tensor`

`cdf`

`copy`

`covariance`

`cross_entropy`

`entropy`

`event_shape_tensor`

`is_scalar_batch`

`is_scalar_event`

`kl_divergence`

`log_cdf`

`log_prob`

`log_survival_function`

`mean`

`mode`

`param_shapes`

`param_static_shapes`

`prob`

`quantile`

`sample`

`stddev`

`survival_function`

`variance`