Akaikes information criterion

{
  "id": "MLC-T-ENV-000968",
  "term": "Akaikes information criterion",
  "field": "Environment",
  "definition": "1. These are criteria that are used in model selection to select the best model from a set of plausible models. One model is better than another model if it has a smaller AIC (or Bayesian information criterion (BIC)) value. AIC is based on Kullback-Leibler distance in information theory, and BIC is based on integrated likelihood in Bayesian theory. If the complexity of the true model does not increase with the size of the data set, BIC is the preferred criterion, otherwise, AIC is preferred. (Burnham and Anderson 1998)\n\n2. Statistical procedure that provides a measure of the goodness-of-fit of a dose-response model to a set of data. AIC = -2 x (LL - p), where LL is the log-likelihood at the maximum likelihood fit, and p is the degrees of freedom of the model (usually, the number of parameters estimated).",
  "abbreviation": "AIC",
  "see_also": [
    "Bayesian information criterion"
  ],
  "references": [
    "Thesaurus of Terms Used in Microbial Risk Assessment",
    "Benchmark Dose Software (BMDS) Glossary of Terms"
  ],
  "url": "https://mlchart.com/terminology/environment/akaikes-information-criterion/"
}