Results 71 to 80 of about 862,083 (298)

Bayesian inference in FMRI

NeuroImage, 2012
Bayesian inference has taken FMRI methods research into areas that frequentist statistics have struggled to reach. In this article we will consider some of the early forays into Bayes and what motivated its use. We shall see the impact that Bayes has had on haemodynamic modelling, spatial modelling, group analysis, model selection and brain ...
openaire   +5 more sources

A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi, Giorgio Ricciardiello Mejia, Andre Metzger, Kang Hyun Ryu, Salonee Marwaha, Eva Grzegorczyk, Li Zhou, Eliana Jacobs, Boris Gilyadov, Claudia Kunney, Liqhwa Ncube, Ankit Parekh, Emmanuel Mignot, Fanny M. Elahi, Joseph Winer, Kathleen Poston, Andreas Brink‐Kjaer, Emmanuel H. During   +17 more
wiley   +1 more source

Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network

IEEE Access
Causal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas.
Weisi Liu, Xiaogang Pan
doaj   +1 more source

Semiparametric Regression Analysis via Infer.NET

Journal of Statistical Software, 2018
We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models.
Jan Luts, Shen S. J. Wang, John T. Ormerod, Matt P. Wand   +3 more
doaj   +1 more source

Towards modelling active sound localisation based on Bayesian inference in a static environment

Acta Acustica, 2021
Over the decades, Bayesian statistical inference has become a staple technique for modelling human multisensory perception. Many studies have successfully shown how sensory and prior information can be combined to optimally interpret our environment ...
McLachlan Glen, Majdak Piotr, Reijniers Jonas, Peremans Herbert   +3 more
doaj   +1 more source

Biopsychosocial Determinants of Hand Function and Its Trajectories Over Five Years in Patients With Hand Osteoarthritis

Arthritis Care &Research, EarlyView.
Objective This study aimed to investigate hand function trajectories over five years in primary hand osteoarthritis (OA). Additionally, determinants of baseline and longitudinal hand function were assessed. Methods A total of 538 patients with both baseline and five‐year study visits were analyzed.
Annemiek V. E. M. Olde Meule, Coen van der Meulen, Jos Runhaar, Frits R. Rosendaal, Margreet Kloppenburg   +4 more
wiley   +1 more source

Causal inference with large-scale assessments in education from a Bayesian perspective: a review and synthesis

Large-scale Assessments in Education, 2016
This paper reviews recent research on causal inference with large-scale assessments in education from a Bayesian perspective. I begin by adopting the potential outcomes model of Rubin (J Educ Psychol 66:688-701, 1974) as a framework for causal inference ...
David Kaplan
doaj   +1 more source

Nonparametric Bayesian inference in applications

Statistical Methods & Applications, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Müeller, Peter, Quintana Quintana, Fernando, Page, Garritt L.   +2 more
openaire   +5 more sources

Bayesian model-based inference of transcription factor activity [PDF]

, 2007
<b>Background:</b> In many approaches to the inference and modeling of regulatory interactions using microarray data, the expression of the gene coding for the transcription factor is considered to be an accurate surrogate for the true ...
Rogers, S, Girolami, M, Rogers, Simon, Girolami, Mark, Khanin, R, Khanin, Raya   +5 more
core   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source