Results 31 to 40 of about 2,193,761 (259)
Objective Bayesian fMRI analysis - a pilot study in different clinical environments
Frontiers in Neuroscience, 2015 Functional MRI (fMRI) used for neurosurgical planning delineates functionally eloquent brain areas by time-series analysis of task-induced BOLD signal changes. Commonly used frequentist statistics protect against false positive results based on a p-value
Joerg eMagerkurth +15 more
doaj +1 more source
Comparing Bayesian and non-Bayesian accounts of human confidence reports. [PDF]
PLoS Computational Biology, 2018 Humans can meaningfully report their confidence in a perceptual or cognitive decision. It is widely believed that these reports reflect the Bayesian probability that the decision is correct, but this hypothesis has not been rigorously tested against non ...
William T Adler, Wei Ji Ma
doaj +1 more source
NeuroImage, 2022 The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus.
Daniel Spencer +4 more
doaj +1 more source
Approximate Bayesian Inference Based on Expected Evaluations [PDF]
, 2023 Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable.
Hammer, Hugo Lewi, Riegler, Michael
core
(Mal)adaptive Mentalizing in the Cognitive Hierarchy, and Its Link to Paranoia
Computational PsychiatryHumans need to be on their toes when interacting with competitive others to avoid being taken advantage of. Too much caution out of context can, however, be detrimental and produce false beliefs of intended harm.
Nitay Alon +5 more
doaj +1 more source
Cortical hierarchies perform Bayesian causal inference in multisensory perception.
PLoS Biology, 2015 To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources.
Tim Rohe, Uta Noppeney
doaj +1 more source
Brain dynamics for confidence-weighted learning.
PLoS Computational Biology, 2020 Learning in a changing, uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions.
Florent Meyniel
doaj +1 more source
Brain-Inspired Hardware Solutions for Inference in Bayesian Networks
Frontiers in Neuroscience, 2021 The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating ...
Leila Bagheriye, Johan Kwisthout
doaj +1 more source
Approximate Bayesian inference for doubly robust estimation [PDF]
, 2015 Doubly robust estimators are typically constructed by combining outcome regression and propensity score models to satisfy moment restrictions that ensure consistent estimation of causal quantities provided at least one of the component models is ...
McCoy, EJ, Graham, DJ, Stephens, DA
core +1 more source
Bayesian Solution Uncertainty Quantification for Differential Equations [PDF]
, 2016 We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed from a ...
Girolami, MA +4 more
core +1 more source