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Full-Text Articles in Physical Sciences and Mathematics
Bayesian Approach To Finding The Most Likely Circuit Structure, Shannon Harms
Bayesian Approach To Finding The Most Likely Circuit Structure, Shannon Harms
Graduate Research Theses & Dissertations
Systems, and their reliabilities, depend on the reliabilities of the components that theyare composed of, and in this paper we want to nd the system structure that is the most likely given observed data. Bayesian methods were utilized in order to discover the posterior means, or observed reliabilities, of both the components and the systems. Assuming the serial and parallel system structures have independent components, we calculated system reliabilities based on observed component reliabilities by using the multiplication and addi- tion probability rules. We are then able to expand upon the numerical comparison method through a maximum likelihood analysis that …
Construction Of Confidence Intervals For Parameter Estimates Of T-Distribution, Margaret Remus
Construction Of Confidence Intervals For Parameter Estimates Of T-Distribution, Margaret Remus
Graduate Research Theses & Dissertations
Constructing confidence intervals is a standard statistical practice to estimate parameters of a distribution. Estimation of the parameters of a t-distribution can be computationally intensive. As a result, there are not clear defined formulas for the construction of confidence intervals for those estimates. Using the Normal confidence interval equation does not offer an accurate interval. Therefore, this paper will outline potential alternative methods for building confidence intervals to estimate the mean of a t-distribution. This paper will offer several methods and simulations to determine which method is best for constructing confidence intervals for varying sample sizes and degrees of freedom. …
Polarization In Social Networks Under A Proposed Model, Ashley L. Seyfried
Polarization In Social Networks Under A Proposed Model, Ashley L. Seyfried
Graduate Research Theses & Dissertations
The mathematical study of opinion dynamics started in 1956, with French. Since the explosion of social media, this study has increased in popularity. In this paper, we propose a new model to apply to a social network graph. With this model, the opinions of actors are in the range [0,1]. We use the Normal probability distribution to model these opinions in conjunction with a group of parameters. We allow for the uncertainty of members in the network. We then ran simulations to find the values of these parameters that do not lead to a network becoming polarized. Different states of …