Bayesian Statistical Modelling

By S. Ganocy. Technometrics, 2002

523 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5c04a1fe9c67d9f70c8dd59b6c5724be1ee25336

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In Artificial Intelligence

Citations in the corpus

Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship (2007, methodology): “To compare model fit across two models that differ in parameters, we employ Bayes factors or log Bayes factors (West and Harrison 1997, Congdon 2001).”
Eliciting Informative Feedback: The Peer-Prediction Method (2005, background): “after signal p (1) p (2) p (3) p (4) p (5) p (6) p (7) p (8) p (9) pr (h) .”
Plant size and resprouting ability: trading tolerance and avoidance of damage? (2006, methodology): “Subsequently, the probability of resprouting was modelled with a Bayesian Generalized Linear Model with binomial error structure (Congdon 2001).”
Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation (2006, background): “…(where multiple parameters are structurally related and can be represented by joint probability models; see Gelman et al. 2003), nonlinear association and classification (for examples see Congdon 2001; Denison et al. 2002), and a large variety of general linear models and clustering techniques.”
Bayesian Posterior Estimation of Logit Parameters with Small Samples (2004, methodology): “…many of the log-linear and logit modelling examples from the BUGS computer program manual (Gilks et al. 1994) make use of normal priors, and Congdon (2001) also suggests using normal priors with mean zero and large variance when estimating binomial logit regression coefficients in the…”
Building statistical models to analyze species distributions. (2006, methodology): “A Bayesian model uses Bayes’ theorem to combine the information in the data with additional, independently available information (the prior) to produce a full probability distribution (posterior distribution) for all parameters (Gelman et al. 1995, Carlin and Louis 2000, Congdon 2001).”
Multimodel inference in ecology and evolution: challenges and solutions (2011, methodology): “…models, reflecting ‘true’ model probabilities given data support, i.e. posterior model probabilities (other information criteria approximate these posterior model probabilities) (Jefferys, 1961 in Congdon, 2006b): BF1;2 ¼ p yjM1ð Þ p yjM2ð Þ ð1Þ where p(y|Mi) is the marginal likelihood of model i.”
Spatial and spatio-temporal models with R-INLA. (2013, methodology): “Here we use the example presented in Congdon (2007) to investigate suicide mortality in n = 32 London boroughs in the period 1989-1993.”
Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation (2005, methodology): “Moreover, credible intervals on the parameters, or any function of the parameters, are more easily interpreted than the concept of confidence intervals of classical statistics [Congdon, 2001].”
Changing philosophies and tools for statistical inferences in behavioral ecology (2009, methodology): “We suspect that it is usually a challenge to distinguish measurement error from biological factors which reduce repeatability although there have been developments in statistical methods dealing with measurements errors (Congdon 2006).”
Hierarchical Bayesian models for inverse problems in heat conduction (2005, background): “(Some figures in this article are in colour only in the electronic version)”