Integrating Markov Model, Bivariate Gaussian Distribution and GPU Based Parallelization for Accurate Real-Time Diagnosis of Arrhythmia Subclasses

Gawde, Purva R.; Bansal, Arvind K.; Nielson, Jeffery A.

doi:10.1007/978-3-030-02686-8_43

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2019

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“…Gaussian modeling is the subject of constant research by statisticians [14], [15], [16]. It is typically used in tasks of pattern recognition [17], [18], [19], [20], [21], [22],computer vision [23], [24], [25], speech and language recognition [26], [27], [28], machine learning [29], [30]; however, it also finds use in very different areas such as medical or technical diagnostics [31], [32], [33], [34], [35], [36].…”

Section: Introductionmentioning

confidence: 99%

Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space

Psutka

2019

Pattern Recognition

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The significant properties of the maximum likelihood (ML) estimate are consistency, normality, and efficiency. While it has been proven that these properties are valid when the sample size approaches infinity, the behavior of an ML estimator when working with small sample sizes is largely unknown. However, in real tasks, we usually do not have sufficient data to completely fulfill the conditions of an optimal ML estimate. The question arises as to what amount of data is required to be able to estimate a Gaussian model that provides sufficiently accurate likelihood estimates. This issue is addressed with respect to the number of dimensions of the pattern space.

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Section: Introductionmentioning

confidence: 99%

Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space

Psutka

2019

Pattern Recognition

View full text Add to dashboard Cite

show abstract

Integrating Markov Model, Bivariate Gaussian Distribution and GPU Based Parallelization for Accurate Real-Time Diagnosis of Arrhythmia Subclasses

Cited by 1 publication

References 14 publications

Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space

Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space

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