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# Module model_selection

source code

Module for the statistical concept of model selection.

 Functions
float
 aic(chi2, k, n) Akaike's Information Criteria (AIC). source code
float
 aicc(chi2, k, n) Small sample size corrected AIC. source code
float
 bic(chi2, k, n) Bayesian or Schwarz Information Criteria. source code
 Variables
__package__ = `'lib'`

Imports: log, RelaxError

 Function Details

### aic(chi2, k, n)

source code

Akaike's Information Criteria (AIC).

The formula is:

```   AIC = chi2 + 2k
```
Parameters:
• `chi2` (float) - The minimised chi-squared value.
• `k` (int) - The number of parameters in the model.
• `n` (int) - The dimension of the relaxation data set.
Returns: float
The AIC value.

### aicc(chi2, k, n)

source code

Small sample size corrected AIC.

The formula is:

```                      2k(k + 1)
AICc = chi2 + 2k + ---------
n - k - 1
```
Parameters:
• `chi2` (float) - The minimised chi-squared value.
• `k` (int) - The number of parameters in the model.
• `n` (int) - The dimension of the relaxation data set.
Returns: float
The AIC value.

### bic(chi2, k, n)

source code

Bayesian or Schwarz Information Criteria.

The formula is:

```   BIC = chi2 + k ln n
```
Parameters:
• `chi2` (float) - The minimised chi-squared value.
• `k` (int) - The number of parameters in the model.
• `n` (int) - The dimension of the relaxation data set.
Returns: float
The AIC value.

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