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Understanding predictive information criteria for Bayesian …
http://www.stat.columbia.edu/~gelman/research/published/waic_understand3.pdf
WEBKeywords: AIC, DIC, WAIC, cross-validation, prediction, Bayes 1. Introduction Bayesian models can be evaluated and compared in several ways. Most simply, any model or set of models can be taken as an exhaustive set, in which case all inference is summarized by the posterior distribution.
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Watanabe–Akaike information criterion - Wikipedia
https://en.wikipedia.org/wiki/Watanabe%E2%80%93Akaike_information_criterion
WEBIn statistics, the widely applicable information criterion (WAIC), also known as Watanabe–Akaike information criterion, is the generalized version of the Akaike information criterion (AIC) onto singular statistical models.
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WAIC function - RDocumentation
https://www.rdocumentation.org/packages/LaplacesDemon/versions/16.1.6/topics/WAIC
WEBWAIC is an extension of the Akaike Information Criterion (AIC) that is more fully Bayesian than the Deviance Information Criterion (DIC). Like DIC, WAIC estimates the effective number of parameters to adjust for overfitting. Two adjustments have been proposed. pWAIC1 is similar to pD in the original DIC.
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Practical Bayesian model evaluation using leave-one-out cross
https://arxiv.org/abs/1507.04544
WEBJul 16, 2015 · Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model using the log-likelihood evaluated at the posterior simulations of the parameter values.
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Widely applicable information criterion (WAIC) — waic • loo
https://mc-stan.org/loo/reference/waic.html
WEBThe waic() methods can be used to compute WAIC from the pointwise log-likelihood. However, we recommend LOO-CV using PSIS (as implemented by the loo() function) because PSIS provides useful diagnostics as well …
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Exploring the Watanabe-Akaike Information Criterion (WAIC): A …
https://medium.com/aimonks/exploring-the-watanabe-akaike-information-criterion-waic-a-paradigm-shift-in-statistical-model-a6096b5614d4
WEBJan 3, 2024 · The Watanabe-Akaike Information Criterion (WAIC), emerging as a vital tool in Bayesian analysis, offers a fresh perspective on model evaluation. This essay aims to unpack the nuances of...
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Interpret WAIC value - Cross Validated
https://stats.stackexchange.com/questions/476710/interpret-waic-value
WEBJul 11, 2020 · 1 Answer. The value by itself isn’t interpretable. It could be close to zero, greater than a million, or even negative. It is only useful for comparing models. Note that your models must be fit to the same data though in order to compare WAIC values.
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A Widely Applicable Bayesian Information Criterion - Journal …
https://www.jmlr.org/papers/volume14/watanabe13a/watanabe13a.pdf
WEBK(w) = (ab + c)2 + a2b4, which is the Kullback-Leibler distance of a neural network model in Example 1.6 of a book (Watan-abe, 2009), where a true distribution is realizable by a statistical model. The prior φ(w) is defined by some …
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Waic Understand - Department of Statistics - Columbia …
http://www.stat.columbia.edu/~gelman/research/unpublished/waic_understand_final.pdf
WEBThe purpose of the present article is to explore AIC, DIC, and WAIC from a Bayesian per-spective in some simple examples. Much has been written on all these methods in both theory and practice and we do not attempt anything like a comprehensive review (for that, see Vehtari and Ojanen, 2012).
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Empirical evaluation of fully Bayesian information criteria
https://link.springer.com/article/10.1007/s41237-022-00167-x
WEBJun 17, 2022 · This study is to evaluate the performance of fully Bayesian information criteria, namely, LOO, WAIC and WBIC in terms of the accuracy in determining the number of latent classes of a mixture IRT model while comparing it to the conventional model via non-random walk MCMC algorithms and to further compare their performance with …
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