Results 31 to 40 of about 377,664 (139)
Bayesian optimization for demographic inference
G3: Genes, Genomes, Genetics, 2022 Abstract Inference of demographic histories of species and populations is one of the central problems in population genetics. It is usually stated as an optimization problem: find a model’s parameters that maximize a certain log-likelihood.
Ekaterina Noskova +1 more
openaire +4 more sources
Bayesian inference of neuronal assemblies [PDF]
PLOS Computational Biology, 2019 In many areas of the brain, both spontaneous and stimulus-evoked activity can manifest as synchronous activation of neuronal assemblies. The characterization of assembly structure and dynamics provides important insights into how brain computations are distributed across neural networks.
Giovanni Diana +2 more
openaire +7 more sources
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling [PDF]
, 2016 We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed ...
Bach, Francis, Dupuy, Christophe
core +3 more sources
On the Differential Privacy of Bayesian Inference
Proceedings of the AAAI Conference on Artificial Intelligence, 2016 We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on probabilistic graphical models.
Zhang, Zuhe +2 more
openaire +6 more sources
Computational Neuropsychology and Bayesian Inference [PDF]
Frontiers in Human Neuroscience, 2018 Computational theories of brain function have become very influential in neuroscience. They have facilitated the growth of formal approaches to disease, particularly in psychiatric research. In this paper, we provide a narrative review of the body of computational research addressing neuropsychological syndromes, and focus on those that employ Bayesian
Karl J. Friston +3 more
openaire +5 more sources
Approximate Decentralized Bayesian Inference [PDF]
, 2014 This paper presents an approximate method for performing Bayesian inference in models with conditional independence over a decentralized network of learning agents.
Campbell, Trevor, How, Jonathan P.
core +1 more source
Object Perception as Bayesian Inference [PDF]
Annual Review of Psychology, 2004 We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of ...
Kersten, Daniel +2 more
openaire +6 more sources
Variational Bayesian inference for linear and logistic regression
, 2019 The article describe the model, derivation, and implementation of variational Bayesian inference for linear and logistic regression, both with and without automatic relevance determination.
Drugowitsch, Jan
core
Automatic Variational Inference in Stan [PDF]
, 2015 Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate.
Blei, David M. +3 more
core
Computational statistics using the Bayesian Inference Engine
, 2012 This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise
Babu +40 more
core +1 more source