
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes
Bayesian methods have proved powerful in many applications for the infer...
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A continuation method in Bayesian inference
We present a continuation method that entails generating a sequence of t...
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Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models
Many problems of lowlevel computer vision and image processing, such as...
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Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator
As in many fields of dynamic modeling, the long runtime of hydrological ...
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The variational Laplace approach to approximate Bayesian inference
Variational approaches to approximate Bayesian inference provide very ef...
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Bayesian inference for network Poisson models
This work is motivated by the analysis of ecological interaction network...
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LinearNonlinearPoisson Neuron Networks Perform Bayesian Inference On Boltzmann Machines
One conjecture in both deep learning and classical connectionist viewpoi...
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Stochastic Variational Bayesian Inference for a Nonlinear Forward Model
Variational Bayes (VB) has been used to facilitate the calculation of the posterior distribution in the context of Bayesian inference of the parameters of nonlinear models from data. Previously an analytical formulation of VB has been derived for nonlinear model inference on data with additive gaussian noise as an alternative to nonlinear least squares. Here a stochastic solution is derived that avoids some of the approximations required of the analytical formulation, offering a solution that can be more flexibly deployed for nonlinear model inference problems. The stochastic VB solution was used for inference on a biexponential toy case and the algorithmic parameter space explored, before being deployed on real data from a magnetic resonance imaging study of perfusion. The new method was found to achieve comparable parameter recovery to the analytic solution and be competitive in terms of computational speed despite being reliant on sampling.
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