James McAllister’s 2003 article, “Algorithmic randomness in empirical data ” claims that empirical data sets are algorithmically random, and hence incompressible. We show that this claim is mistaken. We present theoretical arguments and empirical evidence for compressibility, and discuss the matter in the framework of Minimum Message Length (MML) inference, which shows that the theory which best compresses the data is the one with highest posterior probability, and the best explanation of the data. Key words
This book is the first one that provides a solid bridge between algorithmic information theory and s...
What is the relationship between plausibility logic and the principle of maximum entropy? When does ...
Was previously entitled "Compressible priors for high-dimensional statistics"International audienceW...
According to the minimum description length (MDL) principle, data compression should be taken as the...
University of Minnesota M.S. thesis. June 2018. Major: Computer Science. Advisor: Peter Peterson. 1 ...
The recently introduced theory of compressive sensing (CS) enables the reconstruction of sparse or c...
Understanding generalization in modern machine learning settings has been one of the major challenge...
Algorithmic information theory gives an idealized notion of compressibility that is often presented ...
We critically analyse the point of view for which laws of nature are just a mean to compress data. D...
We study low dimensional complier parameters that are identified using a binary instrumental variabl...
Minimum Description Length (MDL) inference is based on the intuition that understanding the availabl...
Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data and Bi...
While Kolmogorov (1965, 1983) complexity is the accepted absolute measure of information content of ...
The concept of statistical “equitability” plays a central role in the 2011 paper by Reshef et al. (1...
Ignoring practicality, we investigate the ideal form of minimum description length induction where e...
This book is the first one that provides a solid bridge between algorithmic information theory and s...
What is the relationship between plausibility logic and the principle of maximum entropy? When does ...
Was previously entitled "Compressible priors for high-dimensional statistics"International audienceW...
According to the minimum description length (MDL) principle, data compression should be taken as the...
University of Minnesota M.S. thesis. June 2018. Major: Computer Science. Advisor: Peter Peterson. 1 ...
The recently introduced theory of compressive sensing (CS) enables the reconstruction of sparse or c...
Understanding generalization in modern machine learning settings has been one of the major challenge...
Algorithmic information theory gives an idealized notion of compressibility that is often presented ...
We critically analyse the point of view for which laws of nature are just a mean to compress data. D...
We study low dimensional complier parameters that are identified using a binary instrumental variabl...
Minimum Description Length (MDL) inference is based on the intuition that understanding the availabl...
Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data and Bi...
While Kolmogorov (1965, 1983) complexity is the accepted absolute measure of information content of ...
The concept of statistical “equitability” plays a central role in the 2011 paper by Reshef et al. (1...
Ignoring practicality, we investigate the ideal form of minimum description length induction where e...
This book is the first one that provides a solid bridge between algorithmic information theory and s...
What is the relationship between plausibility logic and the principle of maximum entropy? When does ...
Was previously entitled "Compressible priors for high-dimensional statistics"International audienceW...