Questions tagged [bic]
BIC is an acronym for Bayesian Information Criterion. BIC is one method of model comparison. See also AIC
276
questions
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26 views
Test to select best models in production
I've got four models in production and using the average of them as the served prediction. We get ground truth data immediately.
I've optimized them and found the best models during my training/...
0
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1answer
55 views
AIC/BIC vs the rule of “must include lower order interaction”
I am running a series of mixed effect models, which include both linear and quadratic term of a variable T (continuous) and the main IV I (categorical), and facing a dilemma.
Model 2 include ...
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0answers
49 views
AIC/BIC formula wrong in James/Witten?
Reading "An Introduction to
Statistical Learning" (by James, Witten, Hastie and Tibshirani), on p.211 I came across the following formula for BIC in case of linear regression:
$ BIC = \frac{...
0
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1answer
71 views
ARIMA grid search for best $p$, $d$, $q$ orders based on BIC metric
i am new to R, and i was hoping to get some answers to some questions i have
is there a function to loop through a range of ARIMA model to get the best order/seasonal based on BIC/AIC ?
do i have ...
1
vote
1answer
43 views
Calculate AIC value from a given BIC value?
Is it valid, given a certain BIC value (an output from an R package) with known n and k, to transform the BIC value via mathematical manipulation of the formulae for BIC and AIC to get the "...
4
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1answer
91 views
The Ambiguity in Schwarz Information Criterion Definition
Suppose there are 100 countries, $i = 1, 2, ..., 100$.
Let $b_i$ be the median birth weight of all new born boys in country #i in 2019.
Let $g_i$ be the median birth weight of all new born girls in ...
1
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1answer
75 views
Differences between formulas for AIC and BIC
I have a question regarding the information criteria AIC and BIC:
I found different formulas for the AIC/BIC, the common ones including the likelihood $\mathcal{L}$ are
$$AIC = 2K - 2 ln(\mathcal{L})\...
0
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1answer
88 views
Log-Likelihood Computation for AIC & BIC
Considering $n$ observations that an be modelled by a Gaussian error model and two nested motion models with $p = 4$ and $p = 7$ parameters, I want to compute the log likelihoods $L$ given the Maximum ...
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11 views
How to compare count data to determine
I am performing an experiment to check which information criterion performs best, better and least among Akaike Information Criterion (aic), Bayesian Information Criterion (bic) and aicc. I am ...
0
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0answers
16 views
details in X-means algorithm running and Bic
I want to run x-means and needs to make sure I understand fully .
I want to simulate X-means algorithm based on [1] in MATLAB. I have some questions about this algorithm.
X-means Algorithm Steps:
(1) ...
4
votes
1answer
44 views
Why doesn't the formula for the BIC include $\frac{\lambda}{\pi}$ in it?
I've been going through the derivation of the BIC. In Schwarz's original paper (linked below) he arrives at
\begin{align*}
nA-\frac{1}{2}k_j\log\left(\frac{n\lambda}{\pi}\right)+\log\alpha_j
\end{...
1
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1answer
17 views
Adjusting the number of parameters for AIC / BIC calculation in case of correlated predictors
My current understanding: Both AIC and BIC take the number of parameters as input when comparing nested models with a different number of parameters / predictors.
My question: Is it necessary / a good ...
0
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1answer
31 views
Order and interpretation of Gaussian Mixture Model with strong overlap between components
Most examples for Gaussian Mixture Models (GMMs) employ datasets with fairly obvious underlying structure (well-separated clusters). How should one determine the order of a GMM (and interpret the ...
2
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1answer
21 views
AIC/BIC parameter
I'm wondering what parameter BIC estimates. It seems that AIC is estimating the cross entropy of the estimated model and the true model, and asymptotically is estimating the out of sample entropy loss....
0
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0answers
16 views
k means in python with BIC [duplicate]
I am new to ML. I am trying to implement k-means which uses a BIC function that takes cluster and data points as arguments and returns BIC value. I need a function to find best k value that is ...
0
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0answers
14 views
Are there assumptions for using the regsubsets function in R?
I'm doing a multiple linear regression with 24 independent variables. I don't need to check the OLS assumptions because I'm only interested in R^2 that each variable explains. To get a smaller model I ...
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0answers
16 views
Generalization Error and Matrix Factorizations?
This is more of a discussion/conceptual idea but:
Is the notion of generalization error well-defined for a problem such as matrix factorization?
I'm working with tensors/matrices and performing ...
1
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0answers
26 views
Which GARCH model to choose if both are showing same AIC/BIC and log-likelihood?
I am doing a GARCH model for returns under different error distributions using the R rugarch package. However, two models, under the generalised error distribution and the skewed generalised error ...
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0answers
14 views
Calculating SPBIC in R From Lvaan output?
I am starting to believe that my models may not be appropriate for using the normal Bayesian Information Criterion (BIC) to compare the models. I have too many free parameters (ranging from 1000-2000) ...
1
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0answers
36 views
Bayesian Information Criterion (BIC) Applicability
Question
Can you compare BIC results between two models that are the same except for the removal of, say, a block of items from one of the models?
Situation
I'm still in the process of understanding ...
0
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0answers
15 views
Can we upper bound the KL by BIC or AIC criteria?
I would like to know if it is possible to bound the KL (or the Bayesian free energy a.k.a negative log marginal likelihood) by the Bayesian (and or Akaike) information criterion.
Thank you!
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20 views
How to decide if clustering is reasonable (if the number of clusters in a dataset is bigger than one)?
I would like to implement a system that automatically clusters some objects in order to give information whether there is more than one cluster in a specific set of these objects. In other words I ...
0
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0answers
12 views
Understanding the false discovery rate and positive selection rate
I want to know the relationship with EBIC and the FDR and PSR of some GLM, and what these two formulas mean?
EBIC is defined as
$$ EBIC(s) = -2\ell(\hat{\beta}(s)) + v(s)log(n)+2v(s)\gamma\log(P)$$
...
0
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0answers
19 views
How to decide between goodness of fit tests
I have a question on BIC and AIC.
I have a data set and I need to test this data set if it fits various distributions (for example, Gamma or Poisson, etc.) I need to use AIC and BIC statistics for ...
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0answers
68 views
GMM: Negative BIC values decreasing with k due to small penalty
I am performing GMM clustering on 10 million datapoints with 5 features. I am trying to use the BIC score to estimate the number of clusters, however the BIC score continously decreseases as k ...
0
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1answer
24 views
knowing which predictors are significant in a logistic regression model
I am trying to make a logistic regression model based on 5 predictors: 2 of these are categorical and 3 are numerical. The output is simply 1 or 0, and upon performing the Matlab function glmfit(x,y,'...
0
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1answer
40 views
Function for bayesian information criterion (BIC)
I am writing my own python function for the bayesian information criterion (BIC) calculation. What I want to do is to choose between two models that I fitted with a set of discrete xy data points.
I ...
0
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0answers
24 views
Evaluating goodness of fit of a model estimated with EM-algorithm (with AIC or BIC)
I am learning a Hidden Markov Model with time varying transition probabilities depending on different features. I do this by estimating the model parameters with the EM-algorithm. Now I would like to ...
0
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1answer
34 views
Source for BIC with residual sum of squares?
Wikipedia states that under certain circumstances, $\mathrm{BIC} $ can be calculated as:
$$\mathrm{BIC} = n \ln(\mathrm{RSS}/n) + k \ln(n),$$
where $\mathrm{RSS}$ is the residual sum of squares. ...
0
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0answers
19 views
Information Criterion vs. Cross Validation for Time Series
I've seen a few posts regarding this question but was hoping for a thorough explanation and possibly a source to go with it. Why would we prefer IC methods over cross-validation for time series data?
...
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34 views
BIC-Lasso Shrinkage
I am currently reviewing the below paper and was wondering if it was possible to correctly implement the BIC equation for "BIC-LASSO Shrinkage". This doesn't appear to be the same as the typical BIC ...
1
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1answer
125 views
Where is the divide between information criterion (AIC, BIC, etc…) and cross validation?
I've taken a regression class and am now in a machine learning class. In regression, we talk about model selection using adj-R2 and AIC/BIC. In my machine learning class, we primarily select models ...
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0answers
11 views
Performance of TDA vs likelihood ratio test, AIC, BIC, $R^2$ for model selection
Topological data analysis (TDA) uses topological persistence to find which variables are important and to distinguish signal from noise.
TDA can be used for variable selection.
How does TDA perform ...
0
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0answers
17 views
How can I use BIC in R studio where p>n (more predictor variables than sample size)
I am working on a problem that has a sample size of 40, and 200 variables in my data set (Including my Y vector). I am just wondering how I can use R studio to make use of the Bayesian Information ...
2
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1answer
50 views
How to perform model selection with the BIC for correlated observations
How is it possible to use the Bayesian Information Criterion (and more generally, to perform model selection) when observations are correlated ?
I would like to compare the BIC of different models ...
1
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0answers
29 views
BIC/AIC Optimal Values
I was reading this paper (https://dms.umontreal.ca/~augusty/FHMV_paper.pdf) and noticed in their analysis (specifically Tables 2 and 3), the highest AIC and BIC values are highlighted and used as ...
0
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1answer
57 views
Should I compare two models using AIC?
I calculated duration (IV) in seconds using two different ranges (0 to 5s and 0 to 10s). The aim was to find out which range contributes to higher word learning outcomes (dichotomous DV).
I ...
0
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0answers
15 views
A dramatic drop in AIC/BIC values (positive to negative) for a certain solution [duplicate]
I was using Stata running latent profile analysis and got fit indices as follows:
AIC 221.25 / -643.82 / 237.25 and BIC 300.94 / -541.36 / 362.47 respectively for 3/4/5-profile solutions.
Is there ...
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0answers
41 views
Is there an AIC and BIC equivalent for MAP?
I would like to know if there is an AIC or BIC equivalent for maximum a posterior estimation. I'm trying to compare several different models that have been fit using MAP, but I am unsure of the best ...
0
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2answers
102 views
Why can't algorithms avoid overfitting themselves?
So, I understand overfitting (bonus question: precise statistical definition of overfitting?). You don't want to match the noise in your sample.
What I don't understand is why this requires a ...
1
vote
1answer
161 views
Using AIC/BIC within cross-validation for likelihood based loss functions
For a course I am teaching, I am having my students fit a Gaussian mixture model using MLEs via the EM algorithm to a bivariate dataset. I have asked the students to use use cross-validation to choose ...
1
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1answer
537 views
AIC and BIC values by auto.arima and manual ARIMA
A time series of yearly data, I want to compare the AIC and BIC values by auto.arima and manual ARIMA.
...
1
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0answers
54 views
AIC / BIC for Model Selection in Copula Model
I'm trying to select the distributional model of 30 marginals (which are restricted to have the same distributional family) in a copula model. However, I therefore get 30 Likelihood/AIC/BIC values for ...
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0answers
292 views
What is the correct implementation of BIC with residual sum of squares?
BIC is most often calculated by maximizing the log likelihood function. However, it is also possible to calculate BIC with residual sums of squares. This is pretty easy to find online and not an issue ...
0
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1answer
88 views
Calculating the AIC based on histograms for selection of stochastic models
I am modelling a nonlinear stochastic process and have data to compare model output against. My aim is to obtain an evolution equation of the form,
$$\frac{du}{dt} = f(u,\theta_f)+\alpha(u,\theta_\...
0
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1answer
92 views
Clarification on Akaike's IC (AIC) and BIC for Expectation Maximization with time-changing parameters
I apologize in advance for the trivial question, but I need a clarification on the following issue. Suppose I have a generic model in state-space form described as
$$x_{t+1}=\phi_{t} x_{t}+w_{t+1}$$
$...
1
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0answers
69 views
Calculating BIC based on number of parameters in neural network and loss
I have implemented a variational autoencoder. Now, I would like to calculate the Bayesian Information Criterion (BIC) based on the number of parameters in the network and the loss. For example, let's ...
2
votes
2answers
45 views
Test all models possibles is a good manner to choose the “best” model?
I have programmed a function in python to test all possibles linear regression models that I can do with 5 variables. I choose the "best" model in base its AIC and BIC. These models are ...
0
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1answer
57 views
Comparing BIC values from variations of a data set
I have a data set of 10000 obs of insurance claims. I've fitted it to a Gamma distribution after dividing my values by 10^8; to a Log-Normal distribution, without changing anything in my data set; and ...
18
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4answers
695 views
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)....
