jesterTOV.inference.config.schema.SMCRandomWalkSamplerConfig

class SMCRandomWalkSamplerConfig(**data)

Bases: BaseSamplerConfig

Configuration for Sequential Monte Carlo with Random Walk kernel.

Variables:

• type (Literal["smc-rw"]) – Sampler type identifier

• n_particles (int) – Number of particles (default: 10000)

• n_mcmc_steps (int) – Number of MCMC steps per tempering level (default: 1)

• target_ess (float) – Target effective sample size for adaptive tempering (default: 0.9)

• random_walk_sigma (float) – Fixed sigma scaling for Gaussian random walk kernel (default: 1.0). The proposal covariance is computed from particles and scaled by sigma^2. Default of 1.0 uses the empirical covariance directly.

__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.

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, ...])
from_orm(obj)
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, 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, /)	Override this method to perform additional initialization after __init__ and model_construct.
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, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)
validate(value)
validate_base_positive(v)	Validate that value is positive.
validate_fraction(v)	Validate that value is in (0, 1].
validate_positive(v)	Validate that value is positive.
validate_positive_float(v)	Validate that value is positive.

Attributes

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.
type
n_particles
n_mcmc_steps
target_ess
random_walk_sigma
output_dir
n_eos_samples
log_prob_batch_size

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

n_mcmc_steps: int

n_particles: int

random_walk_sigma: float

target_ess: float

type: Literal['smc-rw']

classmethod validate_fraction(v)

Validate that value is in (0, 1].

Return type:

float

classmethod validate_positive(v)

Validate that value is positive.

Return type:

int

classmethod validate_positive_float(v)

Validate that value is positive.

Return type:

float