Results 41 to 50 of about 872,459 (340)

Bayesian alternatives to null hypothesis significance testing in biomedical research: a non-technical introduction to Bayesian inference with JASP

BMC Medical Research Methodology, 2020
Background Although null hypothesis significance testing (NHST) is the agreed gold standard in medical decision making and the most widespread inferential framework used in medical research, it has several drawbacks.
Riko Kelter
semanticscholar   +1 more source

Bayesian inference and hallucinations in schizophrenia [PDF]

Brain, 2019
This scientific commentary refers to ‘Acquisition of visual priors and induced hallucinations in chronic schizophrenia’, by Valton et al. (doi:10.1093/brain/awz171).
Klaas E. Stephan, Klaas E. Stephan, Klaas E. Stephan, Jakob Siemerkus, Sara Tomiello   +4 more
openaire   +4 more sources

Blang: Bayesian Declarative Modeling of General Data Structures and Inference via Algorithms Based on Distribution Continua

Journal of Statistical Software, 2022
Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models,
Alexandre Bouchard-Côté, Kevin Chern, Davor Cubranic, Sahand Hosseini, Justin Hume, Matteo Lepur, Zihui Ouyang, Giorgio Sgarbi   +7 more
doaj   +1 more source

Bayesian inference for psychology. Part II: Example applications with JASP

Psychonomic Bulletin & Review, 2017
Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and ...
E. Wagenmakers, Jonathon Love, M. Marsman, Tahira Jamil, A. Ly, J. Verhagen, Ravi Selker, Q. Gronau, Damian Dropmann, Bruno Boutin, Frans Meerhoff, Patrick Knight, Akash Raj, Erik-Jan van Kesteren, Johnny B van Doorn, Martin Šmı́ra, S. Epskamp, Alexander Etz, D. Matzke, T. de Jong, D. van den Bergh, A. Sarafoglou, H. Steingroever, Koen Derks, Jeffrey N. Rouder, R. Morey   +25 more
semanticscholar   +1 more source

Bayesian parameter inference and model selection by population annealing in systems biology. [PDF]

PLoS ONE, 2014
Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection.
Yohei Murakami
doaj   +1 more source

Variational Inference for Nonlinear Structural ‎Identification [PDF]

Journal of Applied and Computational Mechanics, 2021
Research interest in predictive modeling within the structural engineering community has recently been focused on Bayesian inference methods, with particular emphasis on analytical and sampling approaches. In this study, we explore variational inference,
Alana Lund, Ilias Bilionis, Shirley J. Dyke   +2 more
doaj   +1 more source

Interpreting Generalized Bayesian Inference by Generalized Bayesian Inference

, 2023
The concept of safe Bayesian inference [ 4] with learning rates [5 ] has recently sparked a lot of research, e.g. in the context of generalized linear models [ 2]. It is occasionally also referred to as generalized Bayesian inference, e.g. in [2 , page 1] – a fact that should let IP advocates sit up straight and take notice, as this term is commonly ...
Rodemann, Julian, Augustin, Thomas, de Heide, Rianne   +2 more
openaire   +3 more sources

Enhanced off-grid DOA estimation by corrected power Bayesian inference using difference coarray

The Journal of Engineering, 2019
Sparse Bayesian inference for on-grid direction-of-arrival (DOA) estimation using difference coarray was investigated in the authors’ previous work to estimate more signal sources than the number of physical antenna elements. Sparse Bayesian inference is
Yanan Ma, Xianbin Cao, Xiangrong Wang
doaj   +1 more source

Bias-Corrected Maximum Likelihood Estimation and Bayesian Inference for the Process Performance Index Using Inverse Gaussian Distribution

Stats, 2022
In this study, the estimation methods of bias-corrected maximum likelihood (BCML), bootstrap BCML (B-BCML) and Bayesian using Jeffrey’s prior distribution were proposed for the inverse Gaussian distribution with small sample cases to obtain the ML and ...
Tzong-Ru Tsai, Hua Xin, Ya-Yen Fan, Yuhlong Lio   +3 more
doaj   +1 more source

Bayesian and maximum likelihood phylogenetic analyses of protein sequence data under relative branch-length differences and model violation

BMC Evolutionary Biology, 2005
Background Bayesian phylogenetic inference holds promise as an alternative to maximum likelihood, particularly for large molecular-sequence data sets.
Harlow Timothy J, Mar Jessica C, Ragan Mark A   +2 more
doaj   +1 more source