Results 1 to 10 of about 2,084,039 (283)
Objective and subjective prior distributions for the Gompertz distribution [PDF]
Anais da Academia Brasileira de Ciências, 2018 This paper takes into account the estimation for the unknown parameters of the Gompertz distribution from the frequentist and Bayesian view points by using both objective and subjective prior distributions.
SANKU DEY, FERNANDO A. MOALA
doaj +7 more sources
A method to adjust a prior distribution in Bayesian second-level fMRI analysis [PDF]
PeerJ, 2021 Previous research has shown the potential value of Bayesian methods in fMRI (functional magnetic resonance imaging) analysis. For instance, the results from Bayes factor-applied second-level fMRI analysis showed a higher hit rate compared with ...
Hyemin Han
doaj +3 more sources
On the prior distribution of extinction time. [PDF]
Biol Lett, 2016 Bayesian inference about the extinction of a species based on a record of its sightings requires the specification of a prior distribution for extinction time. Here, I critically review some specifications in the context of a specific model of the sighting record.
Solow AR.
europepmc +4 more sources
Calibrating the prior distribution for a normal model with conjugate prior. [PDF]
J Stat Comput Simul, 2014 For a normal model with a conjugate prior, we provide an in depth examination of the effects of the hyperparameters on the long-run frequentist properties of posterior point and interval estimates. Under an assumed sampling model for the data generating mechanism, we examine how hyperparameter values affect the mean squared error (MSE) of posterior ...
Alber SA, Lee JJ.
europepmc +4 more sources
A Noninformative Prior on a Space of Distribution Functions [PDF]
Entropy, 2017 In a given problem, the Bayesian statistical paradigm requires the specification of a prior distribution that quantifies relevant information about the unknowns of main interest external to the data.
Alexander Terenin, David Draper
doaj +3 more sources
Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters
Entropy, 2021 Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient.
Ruohai Di +3 more
doaj +1 more source
Uncertainty Evaluation Based on Bayesian Transformations: Taking Facies Proportion as An Example
Energies, 2023 Many input parameters in reservoir modeling cannot be uniquely determined due to the incompleteness of data and the heterogeneity of the reservoir. Sedimentary facies modeling is a crucial part of reservoir modeling. The facies proportion is an important
Yangming Qiao, Shaohua Li, Wanbing Li
doaj +1 more source
Frontiers in Psychology, 2023 ObjectiveMuch of psychological research has suffered from small sample sizes and low statistical power, resulting in unstable parameter estimates. The Bayesian approach offers a promising solution by incorporating prior knowledge into statistical models,
Carl Delfin, Carl Delfin
doaj +1 more source
Autocorrelation and Parameter Estimation in a Bayesian Change Point Model
Mathematics, 2023 A piecewise function can sometimes provide the best fit to a time series. The breaks in this function are called change points, which represent the point at which the statistical properties of the model change.
Rui Qiang, Eric Ruggieri
doaj +1 more source
Frontiers in Psychology, 2021 With small to modest sample sizes and complex models, maximum likelihood (ML) estimation of confirmatory factor analysis (CFA) models can show serious estimation problems such as non-convergence or parameter estimates outside the admissible parameter ...
Oliver Lüdtke +4 more
doaj +1 more source