Prior information and uncertainty in inverse problems

By John A. Scales, Luis Tenorio. 2001

214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5edcc5a0fe8b0744a3a734beb7ca4b33c89b42b4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. K. Gallagher
2. Debashish Chowdhury
3. Malcolm Sambridge
4. P. Rickwood
5. Andrew Jackson

In Computational Mechanics

1. Debashish Chowdhury
2. Malcolm Sambridge
3. Christopher C. Pain
4. K. Gallagher

In Statistics and Probability

1. J. A. Connolly
2. Debashish Chowdhury
3. Malcolm Sambridge
4. P. Hopcroft
5. Christopher C. Pain
6. K. Gallagher
7. P. Rickwood
8. Andrew Jackson

Citations in the corpus

• Reconstruction of a distribution from a finite number of moments with an adaptive spline-based algorithm (2010, unknown): “However, improper priors still influence strongly the results of MEM, as shown for example in Kass and Wasserman (1996) and Scales and Tenorio (2001).”
• Inference of past climate from borehole temperature data using Bayesian reversible jump Markov chain Monte Carlo (2007, background): “However, it is impossible to define totally non-informative priors and so sensible choices relating real uncertainties must be made (e.g. Scales & Tenorio 2001).”
• Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems (2009, background): “Scales and Tenorio, 2001; Curtis and Wood, 2004).”
• Bayesian linearized AVO inversion (2003, background): “…Bayesian setting is a natural choice for many geophysical inverse problems, where it is possible to combine available prior knowledge with the information contained in the measured data (Tarantola and Valette, 1982; Tarantola, 1987; Duijndam, 1988a,b; Scales and Tenorio, 2001; Ulrych et al., 2001).”
• Trans-dimensional inverse problems, model comparison and the evidence (2006, background): “For a more expansive summary of the issues and arguments see (Backus 1988a; Scales & Snieder 1997; Scales & Tenorio 2001; Jaynes 2003).”
• 3‐D multiobservable probabilistic inversion for the compositional and thermal structure of the lithosphere and upper mantle. I: a priori petrological information and geophysical observables (2013, background): “Although L(m) usually can be determined in a straightforward manner, the choice of (m) is not trivial and often controversial [e.g., Scales and Tenorio, 2001; Kitanidis, 2011a,2011b].”
• Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, methodology): “For drawing statistical inference regarding a kinetic model, the Bayesian approach has gained increasing popularity in recent years [453, 454, 455, 456, 457, 458].”