# Tagged Questions

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

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### AIC and BIC for Support Vector Machine inside e1071

After training a support using the e1071 package of R, how can I calculate an information criterion such as AIC or BIC?
156 views

### Comparison between MDL and BIC

I'm currently studying Hidden Markov Models. There's a set of observations from which I need to determine the optimal number of states. After having found the maximum likelihood using Baum-Welch, I ...
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### How to interpret BIC

I am fitting two different models to the same data. In one model, there is one free parameter for three different experimental conditions. In another model, I fit three free parameters, one for each ...
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### BIC vs. Out of sample performance

I have two statistical models. Model 1 uses a GLM approach while model 2 uses a time series approach for fitting. I want to compare these two models. Model 1 (i.e. GLM) has a better out of sample ...
198 views

### Calculate BIC to determine the optimal number of clusters (k-means clustering)

I have a set of data and want to know whether they fall in 1, 2 or 3 groups. I started exploring the question by using k-means in MATLAB. By just looking at the distance from the centroid of each ...
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### Bayesian Information Criterion (BIC) for large samples

The Bayesian information criterion is defined as $BIC = -2 \text{ln}(L) + k\text{ln}(n)$, where $L$ is the maximized likelihood of the data, and where $n$ is the sample size. In case of a huge sample ...
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### Two simple or one complex model, BIC and likelihood

I have a set of data points with a total number of Nt. I know a priori that the data comes from two distinct processes (distributions). I am trying to find the optimal model parameters together with ...
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### Computing BIC for SUR model

Consider the following m regression equation system: $$r^i = X^i \beta^i + \epsilon^i \;\;\; \text{for} \;i=1,2,3,..,T$$ where $r^i$ is a $(T\times 1)$ vector of the T observations of the dependent ...
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### Criteria for selecting the “best” model in a Hidden Markov Model

I have a time series data set to which I am trying to fit a Hidden Markov Model (HMM) in order to estimate the number of latent states in the data. My pseudo code for doing this is the following: ...
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### Negative binomial GLM, the most complex model always has lowest AIC (all interaction terms)

I apologize for all those questions on modelling. It is the very first time that I try GLM and I am really lost even after reading a lot of papers. I have divided my covariates according to their ...
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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 ...
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### Bayesian model comparison: What is it about MCMC that makes RSS or BIC hard to use?

I'm trying to figure out why certain methods are used for comparing models in Bayesian statistics. DIC is often used in Bayesian model comparison. However, I'm under the impression that one could ...
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### BIC and AIC(c) and group data

I would like to compare two models using the BIC and AICc. Doing so seems fairly straightforward if both models are fit to only one dataset. However, I have data from 10 participants, and there is no ...
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### Number of free parameters in Gaussian mixture models

When comparing GMM models with different number of components (i.e number of Gaussians) one penalizes the likelihood for the total number of free parameters in the mixture model. If the data is in $D$ ...
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### comparing AIC (or BIC or whatever) between different SETS of models

Suppose I have $m$ competing models. Suppose also that I could classify these models into $s$ sets. For example, I could classify models of migration behavior conditional on climatic conditions by the ...
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### AIC vs BIC vs MDL

I am trying to learn the difference between the three approaches and their applications. a) As I understand, AIC = -LL+K BIC = -LL+(K*logN)/2 Unless I am ...
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### Sample size in BIC

The definition of the Bayesian information criterion is usually given as $BIC = -2 \text{ln}(L) + k\text{ln}(n)$, where $ln(L)$ is the maximized log-likelihood of the data given a particular model, ...
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### lm to lmer function tweaking

I have stolen and modified a snippet of code found off the internet from (http://www.r-bloggers.com/aic-bic-vs-crossvalidation/) which graphically depicts AIC and BIC values for different polynomial ...
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### AIC, BIC parsimony

I've set up code to give me a graphical depiction of AIC vs BIC parsimony over various degrees of polynomial models. On the rare occassion AIC does not match BIC trends, which parsimonious model ...
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### AIC, BIC, DIC, model selection criteria

I am trying to understand the difference between these parameters, and their application. Was hoping to get some correction/clarification to my statements. I have a training set and cross-validation ...
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### K-means & BIC (to validate clusters) in R

I'm wondering if there is a good way to calculate the clustering criterion based on BIC formula, for a k-means output in R? I'm a bit confused as to how to calculate that BIC so that I can compare it ...
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### Hypothesis test for odds ratios

I have two possible exposure variables (A and B) for use in a statistical model predicting a binary health outcome. I have fitted models with each variable separately and now know that one variable ...
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### Calculating the BIC for multidimensional, clustered data

I have some microarray data (~15 samples) which I've clustered via pam, with a range of cluster sizes and I want to find out the optimal k with BIC. I basically want to re-implement the BIC score ...
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### Negative and positive AICc/BIC for two models with transformed data - how to compare?

I am using AICc for model selection for transformed data (continous variable). One model I used $\log_{10}(PWV)$ as response and the other $\log(PWV)$ as response, but I am not sure which one to use ...
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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, ...
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### What to choose from BIC/AIC/ridge/elastic net?

I have the following regression problem I have about 60 independent variables; some of them have a high correlation with others. I have around 3 million observations (1) - My main goal is ...
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### Can AIC determine which data better fit the same model?

Many moons ago, I asked how to differentiate between two very similar non-linear fits and which was better. Finally got that all straightened out after many headaches and three different software ...
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### How to calculate absolute fit indices (RMSEA, GFI…) from relative ones (AIC, BIC…)?

I have conducted an IRT analysis with Conquest in order to compare two models (1-dimensional vs. 8-dimensional) applied to a given data set (41 items of a questionnaire, N=195). Comparing the ...
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### Number of parameters in Markov model

I want to use BIC for HMM model selection: BIC = -2*logLike + num_of_params * log(num_of_data) So how do I count the number of parameters in the HMM model. ...
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### Why do I get equal AIC, BIC and log likelihood for different models in LME framework?

I have two LME models with the same interaction, one containing both main effects and one containing only one main effect, say :  H\_CE = Season + Crownlevel + Season:Crownlevel , random = ...
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### Interpreting AIC and BIC fit

I'm writing a CFA paper, and I have run into some trouble interpreting the AIC and BIC. This is my first paper using continuous variables, thus the first time I will be reporting these fit statistics ...
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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 ...
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### BIC or AIC to determine the optimal number of clusters in a scale-free graph?

I am currently trying to partition a scale-free ("big") graph (around 20k vertices, 500k edges) into appropriate sub-graphs. Having derived the Laplacian of the graph, I tried running an approach ...
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### How to specify the unit information prior

Continuing on from this question and this question re BIC and its approximation to the Bayes factor with a unit information prior (Kass & Wasserman, 1995), I'm trying to quantify this relationship ...
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### The unit information prior and its BIC approximation

Just moments ago, I asked this question because I've been reading Wagenmakers 2007. I now have a better understanding of what a unit information prior is and can push my knowledge further with two (by ...
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### What is a “Unit Information Prior”?

I've been reading Wagenmakers (2007) A practical solution to the pervasive problem of p values. I'm intrigued by the conversion of BIC values into Bayes factors and probabilities. However, so far I ...
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### Is it possible to calculate AIC and BIC for lasso regression models?

Is is possible to calculate an 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 ...
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### Full posterior vs Bayesian Information Criterion for selecting number of HMM states

So I'm looking into methods in selecting the best number of hidden states for a hidden markov model, given I don't know what how many states "generated" my data. One method I've seen a lot is to learn ...
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### Which measure of model fit to report when performing likelihood based regression: AIC, BIC, Pseudo R-square?

I'd like to hear your opinions on the following: What parameters would you report when estimating different likelihood based regression? AIC, BIC, Pseudo $R^2$? What is the standard to report? ...
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### Accounting for discrete or binary parameters in Bayesian information criterion

BIC penalizes based on the number of parameters. What if some of the parameters are some sort of binary indicator variables? Do these count as full parameters? But I can combine $m$ binary parameters ...
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### Does there exist a model fit statistic (like AIC or BIC) that can be used for absolute instead of just relative comparisons?

I'm not that familiar with this literature, so please forgive me if this is an obvious question. Since AIC and BIC depend on maximizing the likelihood, it seems that they can only be used to make ...
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### Why do AIC and BIC show inversed outputs?

I am comparing three relatively simple GLMs having a Gamma distribution with AIC and BIC. The aim is to identify the effects of fertilizers (fdung), year and site on biomass of a specific grass ...
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### Random coefficient models with fractional polynomial transformation selection method, which criterion to use?

I am trying to conduct a meta analysis for dose response studies where I am using fractional polynomial transformation from predefined family of powers. Now data fitting using all possible ...
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### X-mean algorithm BIC calculation question

I'm having trouble understanding some of the formulas in this paper related to BIC calculation (Dan Pelleg and Andrew Moore, X-means: Extending K-means with Efficient Estimation of the Number of ...