Results 21 to 30 of about 767,112 (259)

The individual true and error model: Getting the most out of limited data

Judgment and Decision Making, 2020
True and Error Theory (TET) is a modern latent variable modeling approach for analyzing sets of preferences held by people. Individual True and Error Theory (iTET) allows researchers to estimate the proportion of the time an individual truly holds a ...
Pele Schramm
doaj   +1 more source

Efficient utility-based clustering over high dimensional partition spaces [PDF]

, 2009
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest scoring (MAP) partition is usually impossible.
Smith, JQ, Millar, A. J., Millar, AJ, Liverani, Silvia, Smith, J. Q., Liverani, S, Anderson, Paul E., Anderson, PE, Edwards, Kieron D., Edwards, KD   +9 more
core   +1 more source

A Bayesian Spatial Propensity Score Matching Evaluation of the Regional Impact of Micro-finance

Review of Economic Analysis, 2017
A Bayesian Spatial-Propensity Score Matching estimator is proposed to measure the regional impact of microfinance on poverty reduction and women's empowerment.
Rolando Gonzales, Patricia Aranda, Joel Mendizabal   +2 more
doaj   +1 more source

Approximate Bayesian Inference Based on Expected Evaluations [PDF]

, 2023
Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable.
Hammer, Hugo Lewi, Riegler, Michael
core  

Approximate Bayesian inference for doubly robust estimation [PDF]

, 2015
Doubly robust estimators are typically constructed by combining outcome regression and propensity score models to satisfy moment restrictions that ensure consistent estimation of causal quantities provided at least one of the component models is ...
McCoy, EJ, Graham, DJ, Stephens, DA
core   +1 more source

Bayesian Solution Uncertainty Quantification for Differential Equations [PDF]

, 2016
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed from a ...
Girolami, MA, Campbell, DA, Calderhead, B, ,, Chkrebtii, OA   +4 more
core   +1 more source

Using stacking to average bayesian predictive distributions (with discussion) [PDF]

, 2018
Bayesian model averaging is flawed in the M-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of ...
Grunwald, Peter, Castro-Camilo, Daniela, Li, Meng, Shin, Minsuk, Grushka-Cockayne, Yael, Gelman, Andrew, Lichtendahl, Kenneth C., Iacopini, Matteo, Dawid, A. Philip, van Dijk, Herman K., Lichtendahl, Kenneth C, Yao, Yuling, Zhou, Tianjian, Winkler, Robert L, Aastveit, Knut Are, Dawid, A Philip, Yoo, William Weimin, Pericchi, Luis, Clarke, Bertrand, Bakka, Haakon C, Freni-Sterrantino, Anna, Franck, Christopher T., Hoogerheide, Lennart, Tonellato, Stefano, Winkler, Robert L., Simpson, Daniel, Ferreira, Marco A. R., Franco-Villoria, Maria, Belitser, Eduard, Bakka, Haakon C., van Dijk, Herman K, Ferreira, Marco AR, de Heide, Rianne, Nurushev, Nurzhan, Jose, Victor Richmond R., Vehtari, Aki, Clyde, Merlise, McAlinn, Kenichiro, West, Mike, Rue, Havard, Huser, Raphael, Opitz, Thomas, Jose, Victor Richmond R, Franck, Christopher T   +43 more
core   +1 more source

bayesian: Bindings for Bayesian TidyModels

, 2022
Fit Bayesian models using 'brms'/'Stan' with 'parsnip'/'tidymodels' via 'bayesian' . 'tidymodels' is a collection of packages for machine learning; see Kuhn and Wickham (2020) ). The technical details of 'brms' and 'Stan' are described in Bürkner (2017) ,
Bürkner, Paul-Christian, Badr, Hamada S.   +1 more
core   +1 more source

Bayesian quantification of thermodynamic uncertainties in dense gas flows [PDF]

, 2014
A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the thermodynamic behavior of so-called ...
CINNELLA, Paola, X. Merle, MERLE, Xavier
core   +1 more source

hsbadr/bayesian: bayesian 0.0.2

, 2021
Bindings for Bayesian ...
Hamada S. Badr
core   +1 more source