Results 31 to 40 of about 99,191 (258)

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 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

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

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

The Inverse of Exact Renormalization Group Flows as Statistical Inference

Entropy
We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection–diffusion equation.
David S. Berman, Marc S. Klinger
doaj   +1 more source

Generalized Bayesian Inference Nets Model and Diagnosis of Cardiovascular Diseases

Journal of Intelligent Systems, 2011
A generalized Bayesian inference nets model (GBINM) is proposed to aid researchers to construct Bayesian inference nets for various applications. The benefit of such a model is well demonstrated by applying GBINM in constructing a hierarchical Bayesian ...
Sekar Booma Devi, Dong Mingchui, Dou Jiayi   +2 more
doaj   +1 more source

Baby Boomers in Germany: a secondary data analysis of demographics, regional disparities, healthcare utilization, and mortality

BMC Public Health
Background The demographic shift driven by the aging Baby Boomer cohort represents a substantial burden for our healthcare system. The aim of this study is to analyze the basic demographic characteristics, regional disparities, healthcare utilization ...
Andreas Kuehnl, Volker J. Schmid, Michaela Olm, Wolfgang Weber   +3 more
doaj   +1 more source

Integrating Boolean Satisfiability Algorithms into Bayesian Networks for Accelerated Deterministic Inference

Algorithms
Exact probabilistic inference in Bayesian Networks (BNs) becomes increasingly expensive as network size and structural complexity grow, limiting its applicability in time-sensitive decision-support systems.
Efraín Evaristo Díaz Macías, José Steven Cordero Bazurto, Byron Wladimir Oviedo Bayas   +2 more
doaj   +1 more source

Bayesian surprise shapes neural responses in somatosensory cortical circuits

Cell Reports, 2023
Summary: Numerous psychophysical studies show that Bayesian inference governs sensory decision-making; however, the specific neural circuitry underlying this probabilistic mechanism remains unclear.
Gwendolyn English, Newsha Ghasemi Nejad, Marcel Sommerfelt, Mehmet Fatih Yanik, Wolfger von der Behrens   +4 more
doaj   +1 more source

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   +4 more sources