Results 11 to 20 of about 99,191 (258)
New Frontiers in Bayesian Modeling Using the INLA Package in R
Journal of Statistical Software, 2021 The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models.
Janet Van Niekerk +3 more
doaj +1 more source
On the Geometry of Bayesian Inference [PDF]
Bayesian Analysis, 2019 We provide a geometric interpretation to Bayesian inference that allows us to introduce a natural measure of the level of agreement between priors, likelihoods, and posteriors. The starting point for the construction of our geometry is the simple observation that the marginal likelihood can be regarded as an inner product between the prior and the ...
de Carvalho, Miguel +2 more
openaire +4 more sources
Frontiers in Neuroinformatics, 2018 Recent debates about the conventional traditional threshold used in the fields of neuroscience and psychology, namely P < 0.05, have spurred researchers to consider alternative ways to analyze fMRI data.
Hyemin Han, Joonsuk Park
doaj +1 more source
Energies, 2023 This paper introduces a novel techno-economic feasibility analysis of energy management utilizing the Homer software v3.14.5 environment for an independent hybrid microgrid.
Abdellah Benallal +5 more
doaj +1 more source
Ciência Rural, 2021 : Although the fruit yield has a core importance in Tahiti acid lime breeding programs, other traits stand out among the quality fruit and vegetative traits as ones that still need to be improved in selection of superior genotypes.
Renan Garcia Malikouski +6 more
doaj +1 more source
Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data. [PDF]
PLoS ONE, 2017 Inferring the structure of molecular networks from time series protein or gene expression data provides valuable information about the complex biological processes of the cell.
Zahra Narimani +4 more
doaj +1 more source
Skill rating by Bayesian inference [PDF]
2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009 Systems Engineering often involves computer modelling the behaviour of proposed systems and their components. Where a component is human, fallibility must be modelled by a stochastic agent. The identification of a model of decision-making over quantifiable options is investigated using the game-domain of Chess.
Giuseppe Di Fatta +2 more
openaire +1 more source
Bayesian statistical inference
Statistica, 2017 This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D.
Bruno De Finetti
doaj +1 more source
Bayesian Inference with Projected Densities
SIAM/ASA Journal on Uncertainty Quantification, 2023 Constraints are a natural choice for prior information in Bayesian inference. In various applications, the parameters of interest lie on the boundary of the constraint set. In this paper, we use a method that implicitly defines a constrained prior such that the posterior assigns positive probability to the boundary of the constraint set.
Jasper M. Everink +2 more
openaire +4 more sources
Deep bootstrap for Bayesian inference
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function.
Lizhen Nie, Veronika Ročková
openaire +3 more sources