StudentT#

class zfit.prior.StudentT(ndof, mu, sigma, name=None)[source]#

Bases: BasePrior

Student’s t-distribution prior.

The Student’s t-distribution is a heavy-tailed distribution that approaches the normal distribution as degrees of freedom increase. It’s useful when you want robustness against outliers while maintaining finite variance (unlike the Cauchy distribution).

This prior is suitable for: - Robust inference with outlier resistance - Parameters where extreme values are possible but not as likely as in Cauchy - Alternative to Normal when heavier tails are desired - Location parameters with moderate uncertainty - Regression coefficients in robust modeling

Properties: - Support: All real numbers (-∞, ∞) - Mean = μ (for ndof > 1), undefined for ndof ≤ 1 - Variance = σ²·ndof/(ndof-2) (for ndof > 2) - Approaches Normal(mu, sigma) as ndof → ∞ - Heavier tails than normal for small ndof

Example

>>> # Heavy-tailed prior (like Cauchy but with finite variance)
>>> prior = StudentT(ndof=3, mu=0.0, sigma=1.0)
>>>
>>> # Moderately robust prior
>>> prior = StudentT(ndof=10, mu=5.0, sigma=2.0)

Initialize a Student’s t prior.

Parameters:

  • ndof (float) – Degrees of freedom parameter controlling tail heaviness. Must be positive. Lower values give heavier tails. As ndof → ∞, approaches Normal(mu, sigma).

  • mu (float) – Location parameter (center) of the distribution.

  • sigma (float) – Scale parameter controlling spread. Must be positive.

  • name (str | None) – Optional name for the prior

__eq__(other)#

Compare two priors for equality.

Parameters:

other – Another ZfitPrior instance to compare with

Returns:

True if the priors are equal

Return type:

bool

__hash__()#

Return hash of the prior based on pdf and name.

Returns:

Hash value for the prior

Return type:

int

log_pdf(value=None)#

Return the log probability of the prior at the given value(s).

Parameters:

value – The parameter value(s) to evaluate the log probability at

Returns:

The log probability

sample(n)#

Sample n values from the prior distribution.

Parameters:

n – Number of samples to draw

Returns:

An array of samples