Model selection based on minimum description length

被引：112

作者：

Grünwald, P ^{[1
]}

机构：

[1] CWI, Amsterdam, Netherlands

来源：

JOURNAL OF MATHEMATICAL PSYCHOLOGY | 2000年 / 44卷 / 01期

关键词：

D O I：

10.1006/jmps.1999.1280

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We introduce the minimum description length (MDL) principle. a general principle for inductive inference based on the idea that regularities (laws) underlying data can always be used to compress data. We introduce the fundamental concept of MDL, called the stochastic complexity, and we show how it can be used for model selection. We briefly compare MDL-based model selection to other approaches and we informally explain why we may expect MDL to give good results in practical applications. (C) 2000 Academic Press.

引用

页码：133 / 152

页数：20