Optimal work extraction and the minimum description length principle
نویسندگان
چکیده
منابع مشابه
Minimum Description Length Principle
The minimum description length (MDL) principle states that one should prefer the model that yields the shortest description of the data when the complexity of the model itself is also accounted for. MDL provides a versatile approach to statistical modeling. It is applicable to model selection and regularization. Modern versions of MDL lead to robust methods that are well suited for choosing an ...
متن کاملThe Minimum Description Length Principle and Model Selection in Spectropolarimetry
It is shown that the two-part Minimum Description Length Principle can be used to discriminate among different models that can explain a given observed dataset. The description length is chosen to be the sum of the lengths of the message needed to encode the model plus the message needed to encode the data when the model is applied to the dataset. It is verified that the proposed principle can ...
متن کاملPSI-BLAST pseudocounts and the minimum description length principle
Position specific score matrices (PSSMs) are derived from multiple sequence alignments to aid in the recognition of distant protein sequence relationships. The PSI-BLAST protein database search program derives the column scores of its PSSMs with the aid of pseudocounts, added to the observed amino acid counts in a multiple alignment column. In the absence of theory, the number of pseudocounts u...
متن کاملThe Minimum Description Length Principle in Coding and Modeling
We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the solution to optimum universal coding problems extending Shannon’s basic source coding theorem. The normalized maximized likelihood, mixture, and predictive codings are each shown to achieve the stochastic complexity to ...
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: Journal of Statistical Mechanics: Theory and Experiment
سال: 2020
ISSN: 1742-5468
DOI: 10.1088/1742-5468/abacb3