pyhs3.distributions.MixtureDist

class pyhs3.distributions.MixtureDist(**data)

Mixture of probability distributions.

Implements a weighted combination of multiple distributions following ROOT’s RooAddPdf. Supports both N and N-1 coefficient configurations where \(N\) represents number of distributions (summands):

N-1 coefficients:

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

N coefficients:

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

N coefficients with `ref_coef_norm`:

\[f(x) = \frac{\sum_{i=1}^{n} c_i \cdot f_i(x)}{\sum_{j \in \text{ref\_coef\_norm}} c_j}\]

Parameters:

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

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

• extended (bool) – Whether the mixture is extended (affects normalization). Must be True for N coefficients, False for N-1 coefficients.

• ref_coef_norm (list[str] | None) – Optional list of coefficient names for custom normalization. Only valid when using N coefficients (extended=True).

ROOT Reference:

:rootref:`RooAddPdf <classRooAddPdf.html>`

Parameters:

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, ...])
expected_yield(context)	Compute the total expected yield nu in the extended case.
expression(context)	Evaluate and return a named PyTensor expression.
extended_likelihood(context[, data])	Poisson term for the extended likelihood.
from_orm(obj)
get_parameter_list(context, param_key)	Reconstruct a parameter list from flattened indexed keys.
json(*[, include, exclude, by_alias, ...])
likelihood(context)	Builds a symbolic expression for the mixture distribution.
log_expression(context)	Log-probability combining main likelihood with extended terms.
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, ensure_ascii, ...])	!!! 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, extra, ...])	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])
serialize_model(handler)	Do not serialize ref_coef_norm if it is unspecified (None).
serialize_ref_coef_norm(ref_coef_norm)	Convert list back to comma-separated string for serialization.
split_comma_separated_ref_coef_norm(v)	Convert comma-separated string to list for ref_coef_norm.
update_forward_refs(**localns)
validate(value)
validate_coefficient_count(coefficients, info)	Validate that coefficient count matches summand count appropriately.
validate_extended_matches_coefficients(...)	Validate that extended matches coefficient configuration.
validate_ref_coef_norm_usage(ref_coef_norm, info)	Validate that ref_coef_norm is only used with N=N coefficient case.

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
ref_coef_norm
name