In general they don't in the sense of being fully Bayesian. The mental gymnastics are that: - The objective function of the neural net was a likelihood. - The

In general they don't in the sense of being fully Bayesian. The mental gymnastics are that: - The objective function of the neural net was a likelihood. - The prior was improper. In which case the net is a MAP estimate. A MAP estimate will not give you good uncertainty quantification. Given the application to risk modelling, this seems unlikely to be a trivial departure from a fully Bayesian method.