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Reducer
that computes running variance.
Inherits From: CovarianceReducer
, Reducer
tfp.experimental.mcmc.VarianceReducer(
transform_fn=_get_sample, ddof=0, name=None
)
This is a special case of CovarianceReducer
with event_ndims=0
, provided
for convenience. See CovarianceReducer
for more information.
VarianceReducer
is also meant to fit into the larger streaming MCMC
framework. For more generic streaming variance needs, see
RunningVariance
in tfp.experimental.stats
.
Attributes | |
---|---|
ddof
|
|
event_ndims
|
|
name
|
|
parameters
|
|
transform_fn
|
Methods
finalize
finalize(
final_reducer_state
)
Finalizes covariance calculation from the final_reducer_state
.
Args | |
---|---|
final_reducer_state
|
CovarianceReducerState s that represent the
final running covariance state.
|
Returns | |
---|---|
covariance
|
an estimate of the covariance with identical structure to
the results of self.transform_fn .
|
initialize
initialize(
initial_chain_state, initial_kernel_results=None
)
Initializes a CovarianceReducerState
using previously defined metadata.
For calculation purposes, the initial_chain_state
does not count as a
sample. This is a deliberate decision that ensures consistency across
sampling procedures (i.e. tfp.mcmc.sample_chain
follows similar
semantics).
Args | |
---|---|
initial_chain_state
|
A (possibly nested) structure of Tensor s or Python
list s of Tensor s representing the current state(s) of the Markov
chain(s). It is used to infer the shape and dtype of future samples.
|
initial_kernel_results
|
A (possibly nested) structure of Tensor s
representing internal calculations made in a related TransitionKernel .
|
Returns | |
---|---|
state
|
CovarianceReducerState with cov_state field representing
a stream of no inputs.
|
one_step
one_step(
new_chain_state,
current_reducer_state,
previous_kernel_results=None,
axis=None
)
Update the current_reducer_state
with a new chain state.
Chunking semantics are similar to those of batching and are specified by the
axis
parameter. If chunking is enabled (axis is not None
), all elements
along the specified axis
will be treated as separate samples. If a
single scalar value is provided for a non-scalar sample structure, that
value will be used for all elements in the structure. If not, an identical
structure must be provided.
Args | |
---|---|
new_chain_state
|
A (possibly nested) structure of incoming chain state(s)
with shape and dtype compatible with those used to initialize the
current_reducer_state .
|
current_reducer_state
|
CovarianceReducerState s representing the current
state of the running covariance.
|
previous_kernel_results
|
A (possibly nested) structure of Tensor s
representing internal calculations made in a related
TransitionKernel .
|
axis
|
If chunking is desired, this is a (possibly nested) structure of
integers that specifies the axis with chunked samples. For individual
samples, set this to None . By default, samples are not chunked
(axis is None).
|
Returns | |
---|---|
new_reducer_state
|
CovarianceReducerState with updated running
statistics. Its cov_state field has an identical structure to the
results of self.transform_fn . Each of the individual values in that
structure subsequently mimics the structure of current_reducer_state .
|