Abstract

The inherent behavioral variability exhibited by stochastic systems makes it a challenging task for human experts to manually analyze them. Computational modeling of such systems helps in investigating and predicting the behaviors of their underlying processes but at the same time introduces the presence of several unknown parameters. A key challenge faced in this scenario is to determine the values of these unknown parameters against known behavioral specifications. The solutions that have been presented so far estimate the parameters of a given model against a single specification whereas a correct model is expected to satisfy all the behavioral specifications when instantiated with a single set of parameter values. The main contribution of this thesis is computing a quantitative tightness metric describing how well a given stochastic model satisfies a known probabilistic behavioral specification and later employing that metric to guide a search algorithm in order to estimate all the unknown parameters present in the model such that the model satisfies multiple probabilistic temporal logic behavioral specifications simultaneously; thus, generating a single set of parameter values against multiple specifications. The first step of the presented solution uses a larger mean hypothesis test based statistical model checking technique to estimate the unknown parameters of the given stochastic model against a single probabilistic temporal logic behavioral specification and the second phase of this work extends it by using a multiple hypothesis testing based statistical model checking technique to estimate the parameters against multiple probabilistic behavioral specifications simultaneously. The benchmarks studied, analyzed and experimented on in this study are stochastic rule-based computational models of two biochemical receptors, FceRI and T-cell. Experimental results demonstrate successful parameter estimation of all the unknown parameters present in the two models against three probabilistic temporal logic behavioral specifications each.

Notes

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Graduation Date

2019

Semester

Summer

Advisor

Jha, Sumit Kumar

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

CFE0008091; DP0023267

URL

https://purls.library.ucf.edu/go/DP0023267

Language

English

Release Date

2-15-2021

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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