pysr3.priors module
Prior distributions for model parameters
- class pysr3.priors.GaussianPrior(params: Dict)
Bases:
objectImplements Gaussian Prior for various models
Creates GaussianPrior
- Parameters:
params (dict[str: tuple(float, float)]) – gaussian prior for variances of random effects. Same format as above.
- forget()
Releases all problem-dependent quantities
- Returns:
None
- gradient(x)
Evaluates the gradient of the prior with respect to the vector of fixed effects
- Parameters:
x (ndarray) – vector of parameters
- Returns:
gradient
- hessian(_)
Evaluates Hessian of the prior with respect to the vector of fixed effects
- Returns:
Hessian
- instantiate(problem_columns)
Instantiates a Gaussian prior with problem-dependent quantities
- Parameters:
problem_columns (List[str]) – Names of the columns for a particular dataset. Matches the elements of self.params (dict) with the columns of a particular dataset.
- Returns:
None
- loss(x)
Value of the prior at beta, gamma.
- Parameters:
x (ndarray) – vector of parameters
- Returns:
value of the prior.
- class pysr3.priors.NonInformativePrior
Bases:
PriorImplements a non-informative prior
Creates NonInformativePrior
- forget()
Releases all problem-dependent values
- Returns:
None
- static gradient(_)
Evaluates the gradient of the prior with respect to the vector of fixed effects
- Returns:
gradient
- static hessian(_)
Evaluates Hessian of the prior with respect to the vector of random effects
- Returns:
Hessian w.r.t. (gamma, gamma)
- instantiate(problem)
Instantiates the prior based on the problem
- Parameters:
problem (LMEProblem)
- Returns:
None
- static loss(_)
Value of the prior at beta, gamma.
- Returns:
value of the prior.
- class pysr3.priors.Prior
Bases:
object