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A Bayesian Neural Network (BNN) is a neural network, that trains a distribution over its network parameters using Bayesian inference. Once trained it uses this distribution over parameters to predict a probability distribution in the output space for a single input. BNNs are used in applications which require the quantification of uncertainties or in which multimodal distributions are expected that point-estimate predictions cannot express.^[1] The design of a BNN entails the choice of a functional model ${\textstyle m_{\omega }}$ (the neural network, with model parameters ${\textstyle \omega }$), and a stochastic model which contains both priors ${\textstyle p(\omega )}$ and ${\textstyle p(y|x,\omega )}$.^[2]

Not to be confused with Bayesian Network.

The first use of BNNs was in 1993 by Hinton and Van Camp^[3].

From a Bayesian point of view, the model parameters ${\textstyle \omega }$ of a BNN are treated as latent random variables and the training process is their inference, conditional to the (observed) training data ${\textstyle D}$. The distribution of the completely trained model parameters is directly given by Bayes' theorem

${\displaystyle p(\omega |D)={\frac {p(\omega )\,p(D|\omega )}{p(D)}}}$

In practice, the marginal is often intractable, which makes it necessary to adapt strategies to approximate the true posterior. Once the parameter posterior is computed, other quantities of interest can be computed via marginalisation. For example, the predictive posterior is given by

${\displaystyle p(y|x,D)=\int p(y|x,\omega )\,p(\omega |D)d\omega }$

where ${\textstyle p(y|x,\omega )=m_{\omega }(x)}$is provided by the functional model.

The numerical difficulties that come with the computation of Bayes theorem have given rise to a family of BNN approaches, which can roughly be separated into parametric methods (Variational Inference (VI), Bayes by Backprop) and nonparametric methods (free-form VI, Monte Carlo Dropout (MCD), Direct sampling).

A simplification often performed is to introduce a surrogate posterior ${\textstyle q_{\theta }}$ with closed form and minimize its Kullback-Leibler (KL) divergence to the true parameter posterior with respect to the variational parameters ${\textstyle \theta }$. This is possible because the objective function can be rewritten such that the intractable log marginal likelihood ${\textstyle p(D)}$ is separated from the variational term:${\displaystyle {\begin{aligned}{\text{KL}}(q_{\theta }(\omega )\mid \mid p(\omega |D))&={\text{KL}}(q_{\theta }(\omega )\mid \mid p(\omega ))-\mathbb {E} _{q}[\log p(D|\omega )]+\log p(D)\\&=\underbrace {\mathbb {E} _{q}[\log p(D|\omega )-\log q_{\theta }(\omega )+\log p(\omega )]} _{-{\text{ELBO}}}+\log p(D)\end{aligned}}}$

The quantity to maximize therefore becomes the evidence lower bound (ELBO).

Using the reparameterization trick, ...

This allows the reformulation of each Bayesian update to an optimization problem, which can be solved using established gradient descent methods. This method is known as variational inference.

Common choices for the closed form include the mean-field approach, which assumes a complete factorisation of q, and the more general multivariate normal distribution with a low-rank covariance matrix. [Barber and Bishop (Ensemble Learning in Bayesian Neural Networks)] This choice is the equivalent of a loss function in point estimate ML.^[2]

In cases where the computation of the complete log-likelihood becomes infeasible due to the large volume of training data, VI also works with stochastic gradient descent (stochastic variational inference), where in each Bayesian update only a subset of data is used to approximate the likelihood term.

Another approximation technique is Monte Carlo Dropout (MCD)^[4] where conventional dropout layers are kept enabled during inference time. This allows the sampling of predictions.

In contrast to VI, samplers like MCMC converge to the true posterior without assumptions about its form.[Goan]

Often used Hamiltonian Monte Carlo (HMC) or Langevin Monte Carlo

d^[5]

Neural network Gaussian process

...

BNN are usually computation-heavy in training and inference, due to their need to generate many samples from the posterior distribution. For this reason, the VI method is often only suitable in its mean field form, which trades expressiveness for lower computational complexity.^[5] Further, it was found that VI approaches often underestimate the variance of their predictions. Direct sampling using Markov Chain Monte Carlo (MCMC) scales poorly with the amount of data as by default it requires the processing of the entire training dataset to perform an update. Stochastic MCMC methods, which consume only a subset of the data, have been shown to introduce a bias to the posterior.^[5]

VI: Placed assumptions and restrictions about the form may introduce a bias and induce inaccuracies in predictions.

Category:Bayesian statistics