A First Course in Bayesian Statistical Methods

By Peter D. Hoff. 2009

856 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4f22137d5c1c168108b50e3c0a9bf2022989bc45

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In Artificial Intelligence

Citations in the corpus

Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction (2012, methodology): “However, the birth–death MCMC algorithm is quite complex to implement compared with the fairly simple regression variable selection method described in Hoff (2009), which we have applied.”
Unbiased Markov chain Monte Carlo methods with couplings (2020, methodology): “Christine P. Chai (Microsoft Corporation, Redmond) Bayesian methodology is known to provide biased estimates with a lower mean-squared error (Hoff, 2009), so I am excited to see improvements on removing the bias from Markov chain Monte Carlo (MCMC) kernels.”
Bayesian Estimation and Inference (2015, neutral): “An exemplary book is by Gill (2008), but many others are also very good (e.g., Bolstad, 2007; Christensen, Johnson, & Branscum, 2011; Gelman et al., 2004; Hoff, 2009; Jackman, 2009; Kruschke, 2011; Lee, 2004; Lynch, 2010; for concise pro-Bayesian arguments, see Robert, 2007, chap.”
A Bayesian Approach for Estimating Multilevel Latent Contextual Models (2016, methodology): “The PSR is typically used to diagnose whether the chain has converged, and the MCMCSEPSD ratio is used to assess how well the empirical mean of the chain approximates the posterior mean (Hoff, 2009, p. 101).”
Elements of estimation theory for causal effects in the presence of network interference (2017, methodology): “As is typical for linear estimates [Bickel and Doksum, 2015, Hoff, 2009], the MIV LUE will depend only on the covariance of the prior and not the explicit form of the prior distribution or higher moments so we do not specify these.”
Mixtures of Experts Models (2018, methodology): “Positing a conditional d -variate normal prior N ( µ 0 , Λ 0 ) on the group means µ g , and an inverse Wishart prior IW ( ν 0 , S 0 ) on the group covariances Σ g provides conjugacy for these parameters (Hoﬀ, 2009).”
Disconfirmation Effect on Online Rating Behavior: A Dynamic Analysis (2014, methodology): “A HPD interval describes the information we have about the location of the true parameter after we have observed the data (Hoff 2009).”