pyhs3.distributions.MixtureDist

class pyhs3.distributions.MixtureDist(**data)

Mixture of probability distributions.

Implements a weighted combination of multiple distributions:

\[f(x) = \sum_{i=1}^{n-1} c_i \cdot f_i(x) + (1 - \sum_{i=1}^{n-1} c_i) \cdot f_n(x)\]

The last component is automatically normalized to ensure the coefficients sum to 1.

Parameters:

• coefficients (list[str]) – Names of coefficient parameters.

• summands (list[str]) – Names of component distributions.

• extended (bool) – Whether the mixture is extended (affects normalization).

• data (Any)

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])
copy(*[, include, exclude, update, deep])	Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])
expression(context)	Builds a symbolic expression for the mixture distribution.
from_orm(obj)
get_parameter_list(context, param_key)	Reconstruct a parameter list from flattened indexed keys.
json(*[, include, exclude, by_alias, ...])
model_construct([_fields_set])	Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])	!!! abstract "Usage Documentation"
model_dump(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
model_dump_json(*[, indent, include, ...])	!!! abstract "Usage Documentation"
model_json_schema([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
model_parametrized_name(params)	Compute the class name for parametrizations of generic classes.
model_post_init(context, /)	This function is meant to behave like a BaseModel method to initialise private attributes.
model_rebuild(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
model_validate(obj, *[, strict, ...])	Validate a pydantic model instance.
model_validate_json(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
model_validate_strings(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
process_parameter(param_key)	Process a single parameter that can be either a string reference or numeric value.
process_parameter_list(param_key)	Process a list parameter containing mixed string references and numeric values.
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)

Attributes

constants	Dictionary of PyTensor constants generated from numeric field values.
model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
parameters	Set of parameter names this component depends on.
type
summands
coefficients
extended
name