Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 71 for “"probability model"”.
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A Generalized Probability Model for Quantum Mechanics
Made available in DSpace on 2014-12-09T22:17:14Z (GMT). No. of bitstreams: 1 6500823.pdf: 4190501 bytes, checksum: 06b259ef4d66be1ac4121e06bfd0d61f (MD5) Previous issue date: 1964
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Probability model for a convoy threatened by a submarine launched missile
… against a convoy of ships is considered. The model developed considers several different problem parameters. It enables the calculation of probability of detection of the submarine, probability of killing the submarine before a particular missile is fired, and the expected number of missiles …
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Predictive Probability Model for American Civil War Fortifications using a Geographic Information System
Predictive models have established a niche in the field of archaeology. Valued as tools in predicting potential archaeological sites, their use has increased with development of faster and more affordable computer technology. Predictive models highlight areas within a landscape where archaeological …
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The rank analysis of triple comparisons
General extensions of the probability model for paired comparisons, which was developed by R. A. Bradley and M. E. Terry, are considered. Four generalizations to triple comparisons are discussed. One of these models is used to develop methods of analysis of data obtained from the ranks of items …
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Improved Designs for Application Virtualization
… code blocks to the clients in advance. The probability model on which our prefetch method is based may be very large. To manage such a probability model and the associated hardware resources, we perform an information gain analysis. We draw two lower bounds of the information gain brought by …
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Stochastic dynamic optimization approach for revegetation of reclaimed mine soils under uncertain weather regime
… physically based stochastic dynamic optimization model to assist planners in making decisions concerning mine soil depths and soil mixture ratios required to achieve successful revegetation of mined lands at different probability levels of success, subject to an uncertain weather regime. A …
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Rescoring the Montana Risk Assessment Instrument: A Comparison of Methods
… 31, 2010. The results are based on a Burgess model, a linear probability model, and a logistic regression model. The findings suggest that all three models increased the predictive accuracy of the RAI. The Burgess model and the logistic model showed the greatest improvement. When considering …
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Rescoring the Montana Risk Assessment Instrument: A Comparison of Methods
… 31, 2010. The results are based on a Burgess model, a linear probability model, and a logistic regression model. The findings suggest that all three models increased the predictive accuracy of the RAI. The Burgess model and the logistic model showed the greatest improvement. When considering …
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Extensions and Applications of Ensemble-of-trees Methods in Machine Learning
… Trees (DT) focus on an underlying Bayesian probability model to generate the fits. These new probability model-based approaches show much promise versus their algorithmic counterparts, but also offer substantial room for improvement. The first part of this thesis focuses on methodological …
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Optimal long-term financing under ambiguous volatility
I study a continuous-time principal-agent model with hidden action in which the principal and the agent have ambiguous beliefs about the volatility of the project cash flows. I describe a novel formulation that captures uncertainty about the underlying volatility process show how it affects the …
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Antidepressant Persistence Before and After the Introduction of Online Renewal
… of the chosen methods, a mixed effects Cox model with a random intercept, i.e. a frailty model, and a linear probability model with a difference-in-differences estimator and patient fixed effects. While the results of the frailty models without time controls estimated that the hazard of …
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Conserving dolphins and fishermen: Combining science and local knowledge to reduce cetacean bycatch
… are limited. Difficulties result in developing models that accurately depict the interactions. This study focuses on developing methodology to combine disparate data sources, specifically data from fishery observer programs and interviews of fishermen, to better understand these interactions and …
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Climate Variability Influences Women’s Attitudes Towards Intimate Partner Violence (IPV)
… in the domestic violence module. Using a linear probability model, we find prior year growing degree days (GDD), prior and current year rainfall variability have statistically significant effects. Most interestingly, we find that a one standard deviation (SD) rise in prior year annual rainfall …
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Implied probability distributions : estimation, testing and applications
… for a theoretical equilibrium option pricing model is made and with the use of cross-sections of observed options prices point estimates of the risk-neutral probability densities are obtained. The present study is primarily concerned with the estimation of implied riskneutral densities by …
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Resilience of power distribution systems under extreme weather conditions
… resilience of electric distribution systems. The probability model of wood pole failures is used to develop an outage prediction model that serves to identify vulnerable regions of the network. Following this, the benefits of automatic network reconfiguration to reduce customer outages during …
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Water Quality Modeling of Freshwater Diversions in the Pontchartrain Estuary
A 1-D tidal, salinity and water quality model that analyzes the general effects freshwater diversions have on the water quality of the Pontchartrain Estuary over a 17-year period is presented here. Using the modeled live algae concentrations in conjunction with the algal bloom probability model …
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System regularities in design of experiments and their applications
… robust parameter design methods. A hierarchical probability model is used to capture assumptions about robust design scenarios. A process is presented employing this model to evaluate robust design methods.
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Three essays on nonlinear panel data models and quantile regression analysis
… effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force …
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Multi-resolution modeling of a discrete stochastic process identifies causes of cancer
… genome is a major challenge. Doing so requires modeling the background mutation rate, a highly non-stationary stochastic process, across regions of interest varying in size from one to millions of positions. Here, we present the split-Poisson-Gamma (SPG) distribution, an extension of the …
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The Impact of State Exemptions on Personal Bankruptcy Filings
… the debtor, this paper develops a state level probability model to determine whether the different state exemption amounts affect the probability of filing for bankruptcy. The paper shows that states with more lenient exemption laws will incur a higher bankruptcy rate after controlling for …
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