Minimum description length model selection of multinomial processing tree models
نویسندگان
چکیده
منابع مشابه
Minimum description length model selection of multinomial processing tree models.
Multinomial processing tree (MPT) modeling has been widely and successfully applied as a statistical methodology for measuring hypothesized latent cognitive processes in selected experimental paradigms. In this article, we address the problem of selecting the best MPT model from a set of scientifically plausible MPT models, given observed data. We introduce a minimum description length (MDL) ba...
متن کاملOn the Minimum Description Length Complexity of Multinomial Processing Tree Models.
Multinomial processing tree (MPT) modeling is a statistical methodology that has been widely and successfully applied for measuring hypothesized latent cognitive processes in selected experimental paradigms. This paper concerns model complexity of MPT models. Complexity is a key and necessary concept to consider in the evaluation and selection of quantitative models. A complex model with many p...
متن کاملMinimum Description Length Model Selection Criteria for Generalized Linear Models
This paper derives several model selection criteria for generalized linear models (GLMs) following the principle of Minimum Description Length (MDL). We focus our attention on the mixture form of MDL. Normal or normal-inverse gamma distributions are used to construct the mixtures, depending on whether or not we choose to account for possible over-dispersion in the data. For the latter, we use E...
متن کاملModel Selection Based on Minimum Description Length.
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 ...
متن کاملModel Selection and the Principle of Minimum Description Length
This paper reviews the principle of Minimum Description Length (MDL) for problems of model selection. By viewing statistical modeling as a means of generating descriptions of observed data, the MDL framework discriminates between competing models based on the complexity of each description. This approach began with Kolmogorov’s theory of algorithmic complexity, matured in the literature on info...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Psychonomic Bulletin & Review
سال: 2010
ISSN: 1069-9384,1531-5320
DOI: 10.3758/pbr.17.3.275