Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem

By Alberto Malinverno. 2002

428 citations recorded by the source corpus.

Subfields: Geophysics, Nuclear and High Energy Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ad4ff50c1a95bddc5c7d7880191c9956a907221e

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Citations in the corpus

Trans-dimensional inverse problems, model comparison and the evidence (2006, methodology): “For more detailed discussions of parsimony and examples the reader is referred to Malinverno (2000, 2002) , Denison et al. (2002) and Mackay (2003).”
Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems (2008, background): “Bayesian approaches to inverse problems have received much recent interest [4, 48, 49], with applications ranging from geophysics [50, 51] and climate modeling [52] to heat transfer [20, 53].”
Inference of past climate from borehole temperature data using Bayesian reversible jump Markov chain Monte Carlo (2007, methodology): “Recent applications of RJ-MCMC and Bayesian inference to other geophysical problems include Malinverno (2000, 2002), Andersen et al. (2003), Malinverno & Briggs (2004), Malinverno & Leaney (2005), Stephenson et al. (2006) and Sambridge et al. (2006).”
Mixed linear-non-linear inversion of crustal deformation data: Bayesian inference of model, weighting and regularization parameters (2010, methodology): “…methods to geophysical inverse problems include Mosegaard & Tarantola (1995), Mosegaard et al. (1997), Grandis et al. (1999), Schott et al. (1999), Malinverno (2002), Khan et al. (2007), and Gallagher et al. (2009) (see also Mosegaard & Sambridge (2002) and Sambridge & Mosegaard (2002) for…”
Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems (2009, neutral): “The details of the reversible jump acceptance criterion are discussed in mode detail by Green (1995, 2001, 2003), Malinverno (2002), and Sambridge et al. (2006) and examples of the implementation algorithms for variable dimension problems are given in Charvin et al. (in press-a) and Hopcroft et al.…”
Transdimensional inversion of receiver functions and surface wave dispersion (2012, methodology): “The reversible jump algorithm is described in previous studies [e.g., Malinverno, 2002; Gallagher et al., 2011].”
Transdimensional tomography with unknown data noise (2012, background): “A fundamental property of Bayesian inference is the preference for the least complex explanation for an observation, referred to in the lit- erature as ‘natural parsimony’ (Malinverno 2002).”
Receiver function deconvolution using transdimensional hierarchical Bayesian inference (2014, methodology): “…(1995), applied to the analysis of mixtures with an unknown number of components by Richardson & Green (1997), and later adopted to geoscience by Malinverno (2002)—to find an ensemble of receiver functions whose relative fits to the data have been calculated while simultaneously inferring the…”
Stochastic spectral methods for efficient Bayesian solution of inverse problems (2005, background): “Bayesian approaches to inverse problems have seen much recent interest [3,5,2], with applications ranging from geophysics [6,7] and climate modeling [8] to heat transfer [9,10].”