probly.train.evidential.torch.rpn_prior¶

probly.train.evidential.torch.rpn_prior(shape: Size | tuple[int, ...], device: device) → tuple[Tensor, Tensor, Tensor, Tensor][source]¶

Normal-Gamma prior with zero evidence for Regression Prior Networks.

Constructs an uninformative Normal-Gamma prior used in Regression Prior Networks to regularize out-of-distribution predictions via KL divergence, as proposed by Malinin et al. (2020).

Reference:: Malinin et al., “Regression Prior Networks”, NeurIPS 2020. https://arxiv.org/abs/2006.11590

Parameters:

shape – Shape of the prior parameter tensors (e.g., batch shape).
device – Torch device on which to allocate the tensors.

Returns:

Tuple (mu0, kappa0, alpha0, beta0) of Normal-Gamma prior parameters, each with the specified shape.