Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

2021 ◽  
Author(s):  
Huafeng Xia ◽  
Sheng Xu ◽  
Cheng Zhou ◽  
Feiyan Chen
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Multivariable Systems ◽  
Likelihood Principle ◽  
Maximum Likelihood Principle ◽  
Gradient Parameter

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.

Excess molar volumes of the (cyclohexane + pentane, or hexane, or heptane, or octane, or nonane) systems at the temperature 298.15 K

2007 ◽  
Vol 61 (6) ◽  
Author(s):  
J. Balán ◽  
L. Morávková ◽  
J. Linek
Keyword(s):  
Maximum Likelihood ◽  
Fourth Order ◽  
Excess Molar Volumes ◽  
The Other ◽  
Likelihood Principle ◽  
Molar Volumes ◽  
Maximum Likelihood Principle ◽  
Vibrating Tube Densimeter ◽  
Kister Equation

AbstractThe densities of the (cyclohexane + pentane, or hexane, or heptane, or octane, or nonane) systems were measured at the temperature 298.15 K by means of a vibrating-tube densimeter. Their respective excess molar volumes were calculated and correlated using the fourth-order Redlich—Kister equation, with the maximum likelihood principle being applied in the determination of the adjustable parameters. The values of excess molar volumes were negative for the cyclohexane + pentane system, whereas they were positive for the other systems with the values increasing with the number of carbon atoms in the respective alkane molecules.

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