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Operations Research, Systems Engineering and Industrial Engineering Commons™
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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Analysis With Dynamic Bayesian Networks Compared To Simulation, Aaron J. Salazar
Analysis With Dynamic Bayesian Networks Compared To Simulation, Aaron J. Salazar
Theses and Dissertations
This research compares simulations to Dynamic Bayesian Networks in analyzing situations. The research applies models that have known output mean and variance. Queueing systems have theoretical values of the steady-state mean and variance for the number of entities in the system. Monte Carlo simulation development is broken down into two separate approaches: discrete-event simulation and time-oriented simulation. The discrete-event simulation uses pseudo-random numbers to schedule and trigger future events (i.e. customer arrivals and services) and is based on the generated objects.The time-oriented simulation utilizes fixed-width time intervals and updates the system state according to a stochastic process for the set …
Fundamental Tradeoffs In Estimation Of Finite-State Hidden Markov Models, Justin Le
Fundamental Tradeoffs In Estimation Of Finite-State Hidden Markov Models, Justin Le
UNLV Theses, Dissertations, Professional Papers, and Capstones
Hidden Markov models (HMMs) constitute a broad and flexible class of statistical models that are widely used in studying processes that evolve over time and are only observable through the collection of noisy data. Two problems are essential to the use of HMMs: state estimation and parameter estimation. In state estimation, an algorithm estimates the sequence of states of the process that most likely generated a certain sequence of observations in the data. In parameter estimation, an algorithm computes the probability distributions that govern the time-evolution of states and the sampling of data. Although algorithms for the two problems are …