Marginal likelihood
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A marginal likelihood is a likelihood function that has been integrated over the parameter space. In Bayesian statistics, it represents the probability of generating the observed sample for all possible values of the parameters; it can be understood as the probability of the model itself and is therefore often referred to as model evidence or simply evidence.
Due to the integration over the parameter space, the marginal likelihood does not directly depend upon the parameters. If the focus is not on model comparison, the marginal likelihood is simply the normalizing constant that ensures that the posterior is a proper probability. It is related to the partition function in statistical mechanics.[1]
Concept
Given a set of
The above definition is phrased in the context of Bayesian statistics in which case is called prior density and is the likelihood. The marginal likelihood quantifies the agreement between data and prior in a geometric sense made precise[
Unfortunately, marginal likelihoods are generally difficult to compute. Exact solutions are known for a small class of distributions, particularly when the marginalized-out parameter is the
It is also possible to apply the above considerations to a single random variable (data point) , rather than a set of observations. In a Bayesian context, this is equivalent to the
Applications
Bayesian model comparison
In
It is in this context that the term model evidence is normally used. This quantity is important because the posterior odds ratio for a model M1 against another model M2 involves a ratio of marginal likelihoods, called the Bayes factor:
which can be stated schematically as
- posterior odds = prior odds × Bayes factor
See also
- Empirical Bayes methods
- Lindley's paradox
- Marginal probability
- Bayesian information criterion
This article includes a improve this article by introducing more precise citations. (July 2010) ) |
References
Further reading
- Charles S. Bos. "A comparison of marginal likelihood computation methods". In W. Härdle and B. Ronz, editors, COMPSTAT 2002: Proceedings in Computational Statistics, pp. 111–117. 2002. (Available as a preprint on SSRN 332860)
- de Carvalho, Miguel; Page, Garritt; Barney, Bradley (2019). "On the geometry of Bayesian inference". Bayesian Analysis. 14 (4): 1013‒1036. (Available as a preprint on the web: [1])
- Lambert, Ben (2018). "The devil is in the denominator". A Student's Guide to Bayesian Statistics. Sage. pp. 109–120. ISBN 978-1-4739-1636-4.
- The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay.