Results 51 to 60 of about 758,750 (320)
Hierarchical Bayesian Modeling of Fluid‐Induced Seismicity [PDF]
Geophysical Research Letters, 2017 AbstractIn this study, we present a Bayesian hierarchical framework to model fluid‐induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid‐induced seismicity rate proportional to the rate of injected fluid. The fluid‐induced seismicity rate model depends upon a set of physically meaningful parameters and has been ...
M. Broccardo +4 more
openaire +3 more sources
Frontiers in Marine Science, 2016 We present a Hierarchical Bayesian version of Pollock's Closed Robust Design for studying the survival, temporary-migration, and abundance of marked animals.
Robert William Rankin +5 more
doaj +1 more source
A normative model for Bayesian combination of subjective probability estimates
Judgment and Decision Making, 2023 Combining experts’ subjective probability estimates is a fundamental task with broad applicability in domains ranging from finance to public health. However, it is still an open question how to combine such estimates optimally.
Susanne Trick +2 more
doaj +1 more source
Hallucinations and perceptual inference [PDF]
, 2005 This commentary takes a closer look at how constructive models of subjective perception," referred to by Collerton et al. (sect. 2), might contribute to the Perception and Attention Deficit (PAD) model.
Friston, KJ
core +1 more source
Hierarchical Bayesian Modeling of Pharmacophores in Bioinformatics [PDF]
Biometrics, 2010 One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application,
Mardia, Kanti V. +4 more
openaire +3 more sources
Sensitivity Analysis for Bayesian Hierarchical Models
Bayesian Analysis, 2015 Prior sensitivity examination plays an important role in applied Bayesian analyses. This is especially true for Bayesian hierarchical models, where interpretability of the parameters within deeper layers in the hierarchy becomes challenging. In addition, lack of information together with identifiability issues may imply that the prior distributions for
Roos, Małgorzata +3 more
openaire +4 more sources
Hierarchical Bayesian Models for Latent Attribute Detection in Social Media
International Conference on Web and Social Media, 2011 We present several novel minimally-supervised models for detecting latent attributes of social media users, with a focus on ethnicity and gender. Previouswork on ethnicity detection has used coarse-grained widely separated classes of ethnicity and ...
D. Rao +5 more
semanticscholar +1 more source
Computation, 2022 Dependence in meta-analytic models can happen due to the same collected data or from the same researchers. The hierarchical Bayesian linear model in a meta-analysis that allows dependence in effect sizes is investigated in this paper.
Junaidi +3 more
doaj +1 more source
Computational Brain & Behavior, 2020 Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions ...
Mads L. Pedersen, M. Frank
semanticscholar +1 more source
Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito +14 more
wiley +1 more source