Everything about Bayesian model reduction
Mathematical method for quicker estimation of probable outcomes
Bayesian model reduction is a method for computing the evidence and posterior over the parameters of Bayesian models that differ in their priors. A full model is fitted to data using standard approaches. Hypotheses are then tested by defining one or more 'reduced' models with alternative priors, which usually – in the limit – switch off certain parameters. The evidence and parameters of the reduced models can then be computed from the evidence and estimated (posterior) parameters of the full model using Bayesian model reduction. If the priors and posteriors are normally distributed, then there is an analytic solution which can be computed rapidly. This has multiple scientific and engineering applications: these include scoring the evidence for large numbers of models very quickly and facilitating the estimation of hierarchical models. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Bayesian model reductionA 5-minute orientation
- Skim a recent paper: A two-stage Bayesian model updating framework based on an iterative model reduction technique using modal responsesInfluential work since 2022
- Pick up a classic book: The Elements of Statistical LearningThe most-reprinted book on the subject
- Read the landmark paper: Bayesian model reduction and empirical Bayes for group (DCM) studiesCited 656 times
- Go sideways: Dynamic causal modelingA closely related topic
- Go sideways: Bayesian statisticsA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Bayesian model reduction and empirical Bayes for group (DCM) studies
Data‐driven model reduction for the Bayesian solution of inverse problems
Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation
Model-reduction techniques for Bayesian finite element model updating using dynamic response data
Global estimation of child mortality using a Bayesian B-spline Bias-reduction model
Uncertainty quantification in Bayesian inverse problems with model and data dimension reduction
Attribute Reduction in the Bayesian Version of Variable Precision Rough Set Model
Sparse Bayesian learning with model reduction for probabilistic structural damage detection with limited measurements
🆕 Influential since 2022
A two-stage Bayesian model updating framework based on an iterative model reduction technique using modal responses
An improved Bayesian model updating framework by enhanced iterative model reduction technique in time domain
An adaptive dimension-reduction method-based sparse polynomial chaos expansion via sparse Bayesian learning and Bayesian model averaging
Damage identification in fiber metal laminates using Bayesian analysis with model order reduction
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
📚 Books worth reading
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Intelligent Information and Database SystemsAli Selamat · 2013
The Jackknife and BootstrapJun Shao, Dongsheng Tu · 1995
Data science from scratchJoel Grus · 2015
Evolutionary Computation, Machine Learning and Data Mining in BioinformaticsLeonardo Vanneschi · 2012- 📕Applied data miningGuandong Xu · 2013
Stochastic processesM. M. Rao · 1995
Theory and evaluation of single-molecule signalsEli Barkai · 2008
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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