<i>Revisit to the Likelihood Principle</i>

Masahiro MATSUO

doi:10.4288/jafpos.30.0_67

An All-Batch Loss for Constructing Prediction Intervals

Applied Sciences ◽

10.3390/app11041728 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1728

Author(s):

Hua Zhong ◽

Li Xu

Keyword(s):

Gradient Descent ◽

State Of The Art ◽

Prediction Interval ◽

Feedforward Neural Networks ◽

Important Research ◽

Likelihood Principle ◽

High Quality ◽

Construction Methods ◽

Important Research Topic ◽

Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

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stochprofML: stochastic profiling using maximum likelihood estimation in R

BMC Bioinformatics ◽

10.1186/s12859-021-03970-7 ◽

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.

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