Generalized Linear Models: A Bayesian Perspective

By Martin T. Wells. 2001

148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6163d0b24f791eccd469f2068b409009f352e2e9

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Citations in the corpus

Variational inference in nonconjugate models (2012, unknown): “…1 include Bayesian logistic regression (Jaakkola and Jordan, 1997), Bayesian generalized linear models (Wells, 2001), discrete choice models (Braun and McAuliffe, 2010), Bayesian item response models (Clinton et al., 2004; Fox, 2010), and non-conjugate topic models (Blei and Lafferty, 2006, 2007).”
Bayesian varying-coefficient models using adaptive regression splines (2001, unknown): “This is illustrated in our first application, where no thinning is necessary, whereas Mallick et al. (2000) include only every 100th iterate.”
Bayesian inference for generalized additive mixed models based on Markov random field priors (2001, unknown): “Mallick et al. (1999) propose a Bayesian MARS method forGLMs as an extension of the Bayesian curve tting method of Denison et al. (1998),but, as they state, the sampler has slow convergence.”
Bayesian auxiliary variable models for binary and multinomial regression (2006, methodology): “Binary and polychotomous (or multinomial) regression using Generalised Linear Models (GLMs) is a widely used technique in applied statistics and the Bayesian approach to this subject is well documented (see e.g. Dey, Gosh and Mallick (1999)).”
Restricted Strong Convexity Implies Weak Submodularity (2016, unknown): “Let fOBL be the value at the set selected by the Oblivious algorithm, and let fOPT be the optimal value over all sets of size k.”
Bayesian Treed Generalized Linear Models (2003, unknown): “For the second component of the treed model, the subset models considered are all generalized linear models (Nelder and Wedderburn 1972, McCullagh and Nelder 1989, and Dey, Ghosh and Mallick 2000).”