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Quasi-Random Action Selection In Markov Decision Processes, Samuel D. Walker
Quasi-Random Action Selection In Markov Decision Processes, Samuel D. Walker
Electronic Theses and Dissertations
In Markov decision processes an operator exploits known data regarding the environment it inhabits. The information exploited is learned from random exploration of the state-action space. This paper proposes to optimize exploration through the implementation of quasi-random sequences in both discrete and continuous state-action spaces. For the discrete case a permutation is applied to the indices of the action space to avoid repetitive behavior. In the continuous case sequences of low discrepancy, such as Halton sequences, are utilized to disperse the actions more uniformly.
Revising Common Core Georgia Performance Standards Statistics Lesson Plans To Better Align With Statistical Practice, Rachel Bonilla
Revising Common Core Georgia Performance Standards Statistics Lesson Plans To Better Align With Statistical Practice, Rachel Bonilla
Electronic Theses and Dissertations
In this thesis, lesson plans provided by the Georgia Department of Education are revised to give students better exposure and practice working with real-life data. Three learning tasks and a performance task are presented covering a unit lesson on statistical regression. The development of Georgia statistics curriculum standards are reviewed and presented.
Time Series Analysis Of Stock Prices Using The Box-Jenkins Approach, Shakira Green
Time Series Analysis Of Stock Prices Using The Box-Jenkins Approach, Shakira Green
Electronic Theses and Dissertations
A time series is a sequence of data points, typically measured at uniform time intervals. Examples occur in a variety of fields ranging from economics to engineering, and methods of analyzing time series constitute an important part of Statistics. Time series analysis comprises methods for analyzing time series data in order to extract meaningful characteristics of the data and forecast future values. The Autoregressive Integrated Moving Average (ARIMA) models, or Box-Jenkins methodology, are a class of linear models that are capable of representing stationary as well as nonstationary time series. ARIMA models rely heavily on autocorrelation patterns. This paper will …
Comparison Of Career Statistics And Season Statistics In Major League Baseball, Mark Joseph Ammons
Comparison Of Career Statistics And Season Statistics In Major League Baseball, Mark Joseph Ammons
Electronic Theses and Dissertations
This is a comparison of statistics for some of the best seasons and careers of players from Major League Baseball; using data collected on batting average, at bat to homerun ratio, and earned run average. Two teams were created, composed of season leaders and career leaders, chosen for their outstanding offensive and pitching abilities, and were pitted against one another to determine superiority. These two teams also compared against a team from each era of major league baseball. The season and career leaders challenged, the 1918 Boston Red Sox, 1927 New York Yankees, 1955 Brooklyn Dodgers, 1961 New York Yankees, …