Bayesian Methods for Nonlinear Classification and Regression

By R. Soyer. Technometrics, 2004

473 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/37ba6685c14e60399f4f7e785191da0596486a55

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In Analytical Chemistry

In Statistics and Probability

Citations in the corpus

Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems (2009, unknown): “As mentioned earlier, these are known as hyper-parameters and can also be used when the uncertainty on the observations is not well known also (e.g. Denison et al., 2002; Malinverno and Briggs, 2004; Malinverno and Parker, 2005).”
Transdimensional inverse thermal history modeling for quantitative thermochronology (2012, methodology): “[8] To search the range of possible solutions for the unknown model parameters we use Bayesian Markov chain Monte Carlo [e.g., Gilks et al., 1996; Denison et al., 2002] which is a probabilistic sampling procedure.”
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources (2006, background): “In general, however, complete information cannot be assumed, and according to the Bayesian view [4], the best we can do is to use the expected value of B a tr ,a te given the knowledge of a tr .”
Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions (2014, background): “Bayesian linear model We now consider the implications of the main results in the context of a Bayesian linear regression model (see [13] for a book-length introduction as well as a wealth of practical applications).”
Bayesian hierarchical clustering (2005, methodology): “…has also been a considerable amount of decision tree based work on Bayesian tree structures for classification and regression (Chipman et al., 1998; Denison et al., 2002), but this is not closely related to work presented here. that maximize marginal likelihood under a DirichletMultinomial model.”
Confident Interpretation of Bayesian Decision Tree Ensembles for Clinical Applications (2007, methodology): “subsequently to improve the result of model averaging [5].”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, unknown): “The mathematical algorithms for discrete classification include linear discriminant analysis (LDA) (259, 288, 629), support vector machine (56, 288, 780), ANNs (359), multilayer perceptron (43, 75), hidden Markov models (603, 655), k nearest neighbors classifier (177), and nonlinear Bayesian classif”
A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes (2006, background): “KEY WORDS: Bayesian hierarchical clustering; Gene expression profiles; Microarrays.”
Bayesian auxiliary variable models for binary and multinomial regression (2006, methodology): “Although concentrating here on GLMs, the methods are readily applicable to non-linear modelling using free-knot regression splines (see Denison et al , 2002).”
A new integrated model of noncompensatory and compensatory decision strategies (2004, methodology): “Allowing GNH model parameters to also vary randomly across decision makers or decision contexts (reflecting the influence of unobserved decision maker characteristics) is practicable, especially with Bayesian estimation (Denison et al., 2002).”
Inference of past climate from borehole temperature data using Bayesian reversible jump Markov chain Monte Carlo (2007, methodology): “Overall, this modelling scheme corresponds to what statisticians call an open modelling perspective (Bernardo & Smith 1994; Denison et al. 2002) whereby none of the models used is considered the ‘true’ model.”
Modelling Time Series Extremes (2012, background): “Hastie & Tibshirani (1990), Green & Silverman (1994), Fan & Gijbels (1996), Denison et al. (2002), Ruppert et al. (2003) and Wood (2006) give some entry points to the vast literature on nonparametric regression.”
Flexible modeling of conditional distributions using smooth mixtures of asymmetric student t densities (2010, methodology): “Alternatively, one can use a gprior (Zellner, 1986) β ∼ N [ 0, τ2 β(X ′X)−1 ] and then condition on the restrictions imposed by I; Denison et al. (2002, p. 80-81) discusses the advantages and disadvantages of these two different priors. The g-prior is less appealing in a mixture context since (X ′X)”