Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [bic]

BIC is an acronym for Bayesian Information Criterion. BIC is one method of model comparison. See also AIC

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Is there any reason to prefer the AIC or BIC over the other?

The AIC and BIC are both methods of assessing model fit penalized for the number of estimated parameters. As I understand it, BIC penalizes models more for free parameters than does AIC. Beyond a preference based on the stringency of the criteria,…
russellpierce
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AIC,BIC,CIC,DIC,EIC,FIC,GIC,HIC,IIC --- Can I use them interchangeably?

On p. 34 of his PRNN Brian Ripley comments that "The AIC was named by Akaike (1974) as 'An Information Criterion' although it seems commonly believed that the A stands for Akaike". Indeed, when introducing the AIC statistic, Akaike (1974, p.719)…
Hibernating
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AIC guidelines in model selection

I typically use BIC as my understanding is that it values parsimony more strongly than does AIC. However, I have decided to use a more comprehensive approach now and would like to use AIC as well. I know that Raftery (1995) presented nice guidelines…
Tom Carpenter
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Is it possible to calculate AIC and BIC for lasso regression models?

Is it possible to calculate AIC or BIC values for lasso regression models and other regularized models where parameters are only partially entering the equation. How does one determine the degrees of freedom? I'm using R to fit lasso regression…
Jota
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How can one empirically demonstrate in R which cross-validation methods the AIC and BIC are equivalent to?

In a question elsewhere on this site, several answers mentioned that the AIC is equivalent to leave-one-out (LOO) cross-validation and that the BIC is equivalent to K-fold cross validation. Is there a way to empirically demonstrate this in R such…
russellpierce
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Paradox in model selection (AIC, BIC, to explain or to predict?)

Having read Galit Shmueli's "To Explain or to Predict" (2010) and some literature on model selection using AIC and BIC, I am puzzled by an apparent contradiction. There are three premises, AIC- versus BIC-based model choice (end of p. 300 - start…
Richard Hardy
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AIC & BIC number interpretation

I am looking for examples of how to interpret AIC (Akaike information criterion) and BIC (Bayesian information criterion) estimates. Can negative difference between BICs be interpreted as the posterior odds of one model over the other? How can I…
Juan
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Why isn't Akaike information criterion used more in machine learning?

I just ran into "Akaike information criterion", and I noticed this large amount of literature on model selection (also things like BIC seem to exist). Why don't contemporary machine learning methods take advantage of these BIC and AIC model…
echo
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Does BIC try to find a true model?

This question is a follow-up or attempt to clear up possible confusion regarding a topic I and many others find a bit difficult, regarding the difference between AIC and BIC. In a very nice answer by @Dave Kellen on this topic…
Erosennin
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On George Box, Galit Shmueli and the scientific method?

(This question might seem like it is better suited for the Philosophy SE. I am hoping that statisticians can clarify my misconceptions about Box's and Shmueli's statements, hence I am posting it here). George Box (of ARIMA fame) said: "All models…
Skander H.
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Using BIC to estimate the number of k in KMEANS

I am currently trying to compute the BIC for my toy data set (ofc iris (: ). I want to reproduce the results as shown here (Fig. 5). That paper is also my source for the BIC formulas. I have 2 problems with this: Notation: $n_i$ = number of…
Kam Sen
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AIC, BIC and GCV: what is best for making decision in penalized regression methods?

My general understanding is AIC deals with the trade-off between the goodness of fit of the model and the complexity of the model. $AIC =2k -2ln(L)$ $k$ = number of parameters in the model $L$ = likelihood Bayesian information criterion BIC is…
Ram Sharma
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AIC/BIC: how many parameters does a permutation count for?

Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters. However, what if one of our models (for example, the Mallows…
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Is it possible that the AIC and BIC give totally different model selections?

I'm performing a Poisson Regression model with 1 response variable and 6 covariates. Model selection using AIC results in a model with all covariates as well as 6 interaction terms. The BIC however, results in a model with only 2 covariates and no…
WBM
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Mclust model selection

The R package mclust uses BIC as a criteria for cluster model selection. From my understanding, a model with the lowest BIC should be selected over other models (if you solely only care about BIC). However, when BIC values are all negative, the…
Jon
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