Focused Bayesian prediction

Loaiza-Maya, Rubén; Martin, Gael M.; Frazier, David T.

doi:10.1002/jae.2810

Cited by 24 publications

(16 citation statements)

References 67 publications

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“…; 2) What are the implications for approximate computation if the conventional likelihoodbased paradigm is eschewed altogether, and a generalized, robust Bayesian, or moment-based approach to inference (e.g. Bissiri et al, 2016;Chib et al, 2018;Miller and Dunson, 2019;Loaiza-Maya et al, 2021a) is adopted? ; and 3) What role can approximate computation play in Bayesian prediction?…”

Section: Future Directions For Approximate Methodsmentioning

confidence: 99%

Approximating Bayes in the 21st Century

Martin¹,

Frazier²,

Robert³

2021

Preprint

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The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain 'intractable' statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods, high-dimensional models, and models featuring large data sets. These approximate methods are the subject of this review. The aim is to help new researchers in particular -and more generally those interested in adopting a Bayesian approach to empirical work -distinguish between different approximate techniques; understand the sense in which they are approximate; appreciate when and why particular methods are useful; and see the ways in which they can can be combined.

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Section: Future Directions For Approximate Methodsmentioning

confidence: 99%

Approximating Bayes in the 21st Century

Martin¹,

Frazier²,

Robert³

2021

Preprint

Self Cite

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“…Alternatively, non-proper scoring rules could be used which emphasise a particularly important aspect of inference to the task at hand e.g. the tails of the distribution (Loaiza-Maya et al, 2021). Another variation is to allow more freedom to the critic.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Bayesian Experimental Design Without Posterior Calculations: An Adversarial Approach

2023

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Most computational approaches to Bayesian experimental design require making posterior calculations repeatedly for a large number of potential designs and/or simulated datasets. This can be expensive and prohibit scaling up these methods to models with many parameters, or designs with many unknowns to select. We introduce an efficient alternative approach without posterior calculations, based on optimising the expected trace of the Fisher information, as discussed by Walker (2016). We illustrate drawbacks of this approach, including lack of invariance to reparameterisation and encouraging designs in which one parameter combination is inferred accurately but not any others. We show these can be avoided by using an adversarial approach: the experimenter must select their design while a critic attempts to select the least favourable parameterisation. We present theoretical properties of this approach and show it can be used with gradient based optimisation methods to find designs efficiently in practice.

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“…The use of scoring rules for Bayesian updating for parameters was pioneered by Bissiri et al (2016) (rather than inference about models in forecast combination) and is justified in a M-open or misspecified setting. Loaiza-Maya et al (2021) extend this approach to econometric forecasting. They both consider sums which are equally weighted (i.e.…”

Section: Loss Discounting Frameworkmentioning

confidence: 99%

A loss discounting framework for model averaging and selection in time series models

Bernaciak¹,

Griffin²

2022

Preprint

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We introduce a Loss Discounting Framework for forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. The framework allows large scale model averaging/selection and is also suitable for handling sudden regime changes. This novel and simple model synthesis framework is compared to both established methodologies and state of the art methods for a number of macroeconomic forecasting examples. We find that the proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.

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Focused Bayesian prediction

Cited by 24 publications

References 67 publications

Approximating Bayes in the 21st Century

Approximating Bayes in the 21st Century

Bayesian Experimental Design Without Posterior Calculations: An Adversarial Approach

A loss discounting framework for model averaging and selection in time series models

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