Editing Gaussian process (section)

=== Bayesian neural networks as Gaussian processes ===
{{further|Neural network Gaussian process}}
Bayesian neural networks are a particular type of [[Bayesian network]] that results from treating [[deep learning]] and [[artificial neural network]] models probabilistically, and assigning a [[Prior probability|prior distribution]] to their [[Statistical parameter|parameters]]. Computation in artificial neural networks is usually organized into sequential layers of [[artificial neuron]]s. The number of neurons in a layer is called the layer width. As layer width grows large, many Bayesian neural networks reduce to a Gaussian process with a [[Closed-form expression|closed form]] compositional kernel. This Gaussian process is called the Neural Network Gaussian Process (NNGP) (not to be confused with the Nearest Neighbor Gaussian Process <ref name="DattaEtAl2016"></ref>).<ref name="gpml"/><ref name="novak2020">{{cite journal |last1=Novak |first1=Roman |last2=Xiao |first2=Lechao |last3=Hron |first3=Jiri |last4=Lee |first4=Jaehoon |last5=Alemi |first5=Alexander A. |last6=Sohl-Dickstein |first6=Jascha |last7=Schoenholz |first7=Samuel S. |title=Neural Tangents: Fast and Easy Infinite Neural Networks in Python |journal=International Conference on Learning Representations |date=2020|arxiv=1912.02803 }}</ref><ref>{{Cite book|last=Neal|first=Radford M.|title=Bayesian Learning for Neural Networks|publisher=Springer Science and Business Media| year=2012}}</ref> It allows predictions from Bayesian neural networks to be more efficiently evaluated, and provides an analytic tool to understand [[deep learning]] models.