How to sample from the posterior distribution using the Bayesian module?

[sgbaird]

I didn't notice anything obvious within modnet/models/bayesian.py

[ppdebreuck]

The Bayesian module is somewhat deprecated, as the results didn't turn out as good as hoped and the EnsembleMODNetModel should be preferred.

Nevertheless, sampling can be done using model.predict(x) (single outcome, should be repeated to form Monte-Carlo experiment) or model(x) giving a tensorflow_probability.distributions.Distribution. With model the Keras model (not the MODNet one). Currently the BayesianMODNetModel takes 1000 samples and computes the mean and std.

modnet/modnet/models/bayesian.py

Lines 316 to 320 in 6a5b139

	for i in range(1000):
	p = self.model.predict(x)
	if len(self.targets_flatten) == 1:
	p = np.array([p])
	all_predictions.append(p)

Anyway, if you have some ideas to improve, or successful applications with this module, I would be happy to discuss !

[sgbaird]

Ah, gotcha. Thank you! That's too bad about the results. I've been looking into converting (my fork of) CrabNet into a Bayesian transformer network via e.g. BayesFormers, but it seems like it could be pretty involved, so I was pleased to see MODNet had some Bayesian facilities already in place.

Have you thought about trying bayesian.py, ensemble.py, and FitGenetic under a single routine (i.e. "ensemble of genetically optimized Bayesian models")? In this case, sampling from the posterior might just look like taking the average of a single posterior sample from each model within the ensemble.

[ppdebreuck]

No, I haven't spent much time on the Bayesian model to be honest. Might be something to explore in the future ...