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Everything about Minimum description length
Model selection principle
Minimum description length (MDL) is a model selection principle where the shortest description of the data is judged to be the best model. MDL methods learn through a data compression perspective and are sometimes described as mathematical applications of Occam's razor. The MDL principle can be extended to other forms of inductive inference and learning, for example to estimation and sequential prediction, without explicitly identifying a single model of the data. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Minimum description lengthA 5-minute orientation
- Skim a recent paper: A Short Review on Minimum Description Length: An Application to Dimension Reduction in PCAInfluential work since 2022
- Pick up a classic book: The minimum description length principleThe most-reprinted book on the subject
- Read the landmark paper: The minimum description length principle in coding and modelingCited 715 times
- Go sideways: Minimum message lengthA closely related topic
- Go sideways: Solomonoff's theory of inductive inferenceA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
The minimum description length principle in coding and modeling
Model Selection and the Principle of Minimum Description Length
A minimum description length approach to statistical shape modeling
Wavelet minimum description length detrending for near-infrared spectroscopy
Substructure Discovery Using Minimum Description Length and Background Knowledge
Minimum description length induction, Bayesianism, and Kolmogorov complexity
PSI-BLAST pseudocounts and the minimum description length principle
Model Selection Based on Minimum Description Length
🆕 Influential since 2022
A Short Review on Minimum Description Length: An Application to Dimension Reduction in PCA
The minimum description length principle for pattern mining: a survey
Network Reconstruction via the Minimum Description Length Principle
Understanding dual process cognition via the minimum description length principle
Adaptive Multitimescale Event Detection in Nonintrusive Load Monitoring Based on Minimum Description Length Principle
📚 Books worth reading
The minimum description length principlePeter D. Grünwald · 2007
Advances in minimum description lengthPeter D. Grünwald, Mark A. Pitt · 2005
Image to InterpretationMelissa Terras · 2006
IMAGE TO INTERPRETATION: AN INTELLIGENT SYSTEM TO AID HISTORIANS IN READING THE VINDOLANDA TEXTSMELISSA M. TERRAS · 2006
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Minimum message length Minimum message length (MML) is a Bayesian information-theoretic method for statistical model comparison and selection. It provides a formal information…
- Solomonoff's theory of inductive inference Solomonoff's theory of inductive inference in philosophy is a method of evaluating scientific models according to their description length. According to…
- Statistical inference Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis…
- Bayes factor The Bayes factor (BF) is a ratio of two competing statistical models represented by their evidence, and is used to quantify the support for one model over…
- Occam learning In computational learning theory, Occam learning is a model of algorithmic learning where the objective of the learner is to output a succinct representation…
- Inductive probability Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematica…
- Kolmogorov structure function In 1973, Andrey Kolmogorov proposed a non-probabilistic approach to statistics and model selection. Let each datum be a finite binary string and a model…
- Model selection Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context…