Information Theory and an Extension of the Maximum Likelihood Principle

1992 ◽  
pp. 610-624 ◽  
Author(s):  
Hirotogu Akaike
Keyword(s):  
Information Theory ◽  
Maximum Likelihood ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle

scholarly journals stochprofML: stochastic profiling using maximum likelihood estimation in R

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lisa Amrhein ◽  
Christiane Fuchs
Keyword(s):  
Maximum Likelihood ◽  
Cell Fate ◽  
Likelihood Estimation ◽  
R Package ◽  
Cell Populations ◽  
Simulation Studies ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Molecular Expression ◽  
Mixed Samples

Abstract Background Tissues are often heterogeneous in their single-cell molecular expression, and this can govern the regulation of cell fate. For the understanding of development and disease, it is important to quantify heterogeneity in a given tissue. Results We present the R package stochprofML which uses the maximum likelihood principle to parameterize heterogeneity from the cumulative expression of small random pools of cells. We evaluate the algorithm’s performance in simulation studies and present further application opportunities. Conclusion Stochastic profiling outweighs the necessary demixing of mixed samples with a saving in experimental cost and effort and less measurement error. It offers possibilities for parameterizing heterogeneity, estimating underlying pool compositions and detecting differences between cell populations between samples.

Gauss’ law of error revisited in the framework of Sharma-Taneja-Mittal information measure

Open Physics ◽  
2009 ◽  
Vol 7 (3) ◽  
Author(s):  
Antonio Scarfone ◽  
Hiroki Suyari ◽  
Tatsuaki Wada
Keyword(s):  
Maximum Likelihood ◽  
Power Law ◽  
Information Measure ◽  
Asymptotic Power ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Error Distributions ◽  
Related Algebra

AbstractWe reformulate the Gauss’ law of error in presence of correlations which are taken into account by means of a deformed product arising in the framework of the Sharma-Taneja-Mittal measure. Having reviewed the main proprieties of the generalized product and its related algebra, we derive, according to the Maximum Likelihood Principle, a family of error distributions with an asymptotic power-law behavior.

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