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DistributionPDF

Calculate the probability density of a wired-in probability distribution at the given point(s) in event space, by default in log space (i.e

., log probability) for numerical stability. This is referred to as either the probability density function (PDF) if the distribution is continuous, or probability mass function (PMF) if the distribution is discrete. In either case, this is the analytic, i.e. exact, result. This is generally a scalar, but the value will be an array if if the distribution itself is batched, as in, is parameterized by a batch of parameters (e.g., multiple sets of locations or scales) and/or if the given value is itself a batch. In these cases, the shape of the result is (sample_shape, batch_shape) where sample_shape is the shape of the value without the last n dimension(s) if the distribution is multivariate (n=1) or matrix-variate (n=2). Note that the PDF is typically not readily available for sampling-based distributions (e.g., those output by one of the Inference nodes). More Info... Version 1.0.0

Ports/Properties

dist

Distribution to use.

verbose name
Dist
default value
None
port type
DataPort
value type
Distribution (can be None)
data direction
IN

value

Value at which to evaluate the PDF, and result.

verbose name
Value
default value
None
port type
DataPort
value type
AnyNumeric (can be None)
data direction
INOUT

log_space

Return the log probability, otherwise the probability itself. The log is numerically better behaved for small values.

verbose name
Log Space
default value
True
port type
BoolPort
value type
bool (can be None)

set_breakpoint

Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.

verbose name
Set Breakpoint (Debug Only)
default value
False
port type
BoolPort
value type
bool (can be None)

metadata

User-definable meta-data associated with the node. Usually reserved for technical purposes.

verbose name
Metadata
default value
{}
port type
DictPort
value type
dict (can be None)