作者: Jan Levenets , Anna Novikovskaya , Sofia Panteleeva , Zhanna Reznikova , Boris Ryabko
DOI: 10.3390/MATH8040579
关键词: Statistical hypothesis testing 、 Behavioral pattern 、 Ethology 、 Artificial intelligence 、 Mathematical statistics 、 Machine learning 、 Data compression 、 Association (psychology) 、 Pairwise comparison 、 Animal behavior 、 Computer science
摘要: One of the main problems in comparative studying animal behavior is searching for an adequate mathematical method evaluating similarities and differences between behavioral patterns. This study aims to propose a new tool evaluate ethological species. We developed compression-based homogeneity testing classification investigate hunting small mammals. A distinction this approach that it belongs framework statistics allows one compare structural characteristics any texts pairwise comparisons. To validate method, we compared behaviors different species mammals as “texts.” do this, coded elements with letters. then tested hypothesis whether sequences “texts” are generated either by single source or ones. Based on association coefficients obtained from comparisons, built types behaviors, which brought unique insight into how particular rodents changed evolved. suggest relevant evolutionary analysis.