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ConceptPPD / Posterior Predictive Distribution

Bayesian Posterior Predictive Distribution

The core output of Bayesian inference, describing the complete probability distribution over the label of a new sample given observed data. Its computation requires integrating over the posterior distribution of model parameters.

Timeline (last 90 days)

Oct 12

The core idea of Prior-Fitted Networks (PFN) is to enable neural networks to directly learn to approximate the Bayesian posterior predictive distribution (PPD) by pretraining on large amounts of synthetic prior data.

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The core idea of Prior-Fitted Networks (PFN) is to enable neural networks to directly learn to approximate the Bayesian posterior predictive distribution (PPD) by pretraining on large amounts of synthetic prior data.

50%

Source Articles

How Well Do PFNs Approximate Bayesian Inference? An Empirical Analysis of Amortization ErrorOct 12