2001/05/24 by Kenneth P. Burnham, David R. Anderson · 4 citations
Computer Science · Environmental Science · Mathematics · #A priori and a posteriori #Artificial intelligence #Bayesian Methods and Mixture Models #Biology #Computer science #Data mining #Ecology #Econometrics #Inference #Machine learning #Mathematics #Model selection #Null hypothesis #Null model #Set (abstract data type) #Soil Geostatistics and Mapping #Statistical Methods and Bayesian Inference #Statistics
paper · pdf · doi:10.1071/wr99107
openalex publication_date 2001/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We describe an information-theoretic paradigm for analysis of ecological data, based on Kullback–Leibler information, that is an extension of likelihood theory and avoids the pitfalls of null hypothesis testing. Information-theoretic approaches emphasise a deliberate focus on the a priori science in developing a set of multiple working hypotheses or models. Simple methods then allow these hypotheses (models) to be ranked from best to worst and scaled to reflect a strength of evidence using the likelihood of each model ( g i), given the data and the models in the set (i.e. L ( g i | data )). In addition, a variance component due to model-selection uncertainty is included in estimates of precision. There are many cases where formal inference can be based on all the models in the a priori set and this multi-model inference represents a powerful, new approach to valid inference. Finally, we strongly recommend inferences based on a priori considerations be carefully separated from those resulting from some form of data dredging. An example is given for questions related to age- and sex-dependent rates of tag loss in elephant seals ( Mirounga leonina ).