reno.ops.Observation

class reno.ops.Observation(ref, sigma=1.0, data=None)

Bases: Distribution

Represents a Normal distribution around an observed value.

Should only be used for supplying observational data with likelihoods to bayesian models constructed with model.pymc()

Parameters:

• ref (reno.components.Reference) – The equation to supply an observed value for.

• sigma (float) – The std dev to use for the likelihood Normal distribution.

• data (list) – The actual observed data to apply.

Methods

__init__(ref[, sigma, data])	Create a distribution equation part, this is likely being called from a subclass.
add_tensors(pymc_model)
astype(dtype)	Returns a symbolic operation to convert the output of this equation to the specified type.
clean_part_repr(part_index)	Get a non-"Scalar()" string version of a particular sub equation part.
clip(min, max)	Returns a symbolic operation for enforcing the output is between the passed min and max.
equal(obj)	Returns a symbolic operation for checking equality with passed object.
eval([t, save, force])	Get the vector of samples pulled from the probability distribution.
find_parts_of_type(search_type[, ...])	Recursively search for all EquationParts in the tree of the specified type.
get_shape()	Get the size of the additional "data" dimension.
get_type()	Get the type of the target output of this equation expression.
is_static()	Convenience shortcut for reno.utils.is_static() - True if this equation doesn't rely on any dynamic values (thus constant), False if it does.
latex(**kwargs)	Construct a string representation of this portion of the equation for use in a latex display.
mean([axis])	Returns a symbolic operation to find the series-wise mean of the array.
not_equal(obj)	Returns a symbolic operation for checking inequality with passed object.
populate(n[, steps, dim])	Generate n x dim samples based on this probability distribution, assigns as a vector/matrix to self.value.
pt(**refs)	Get a pytensor graph representing this piece of an equation.
pt_str(**refs)	Construct a string containing relevant pytensor code for this piece of the equation.
seek_refs([include_ref_types])	Recursively find a list of all References immediately underneath this part.
series_max()	Returns a symbolic operation to find the series-wise maximum of the array.
series_min()	Returns a symbolic operation to find the series-wise minimum of the array.
sum([axis])	Returns a symbolic operation to find the series-wise sum of the array.

Attributes

dtype	The type of each underlying value.
shape	The size of the data dimension, 1 by default.
timeseries	Returns symbolic operation for getting a timeseries view of the data.

__annotations__ = {}

__module__ = 'reno.ops'

add_tensors(pymc_model)

Parameters:

pymc_model (Model)

Return type:

None

pt(**refs)

Get a pytensor graph representing this piece of an equation.

Parameters:

refs (dict[str, TensorVariable])

Return type:

TensorVariable

pt_str(**refs)

Construct a string containing relevant pytensor code for this piece of the equation. This is useful for “compiling” into pymc code.

Parameters:

refs (dict[str, str])

Return type:

str