![]() Douglas Creelman, in 'PEST: Efficient Estimates on Probability Functions' (Journal of the Acoustical Society of America, Volume 41, Number 4, 1967). We further demonstrate that much better dependence |Θ| may be obtained for this algorithm, depending on the specific information structure of the problem. PEST: Parameter Estimation by Sequential Testing Introduction The PEST algorithm is based on the procedure described by M. Chapter 13: Test Sample Size Determination. ![]() ![]() Chapter 12: Accelerated Reliability and Life Tests. Stats Engine operates by combining sequential testing and false discovery rate control to give you trustworthy results faster, regardless of sample size. Chapter 11: Bayesian MTBF and Reliability Demonstration Tests. Chapter 10: Sequential Testing for the Binomial Case. Chapter 9: Accept-Reject Tests for the Binomial Case. We establish that, with high probability, the total mistake bound for the algorithm is linear (up to a logarithmic term) in the cardinality |Θ| of the parameter set, independently of the cardinality of the state and action spaces. Chapter 8: Sequential Testing on the Scale Parameter of the Weibull Distribution. The forward() method of Sequential accepts any input and forwards it to the first module it contains. ![]() Alternatively, an OrderedDict of modules can be passed in. The proposed algorithm relies on Wald's Sequential Probability Ratio Test to eliminate unlikely parameters, and uses an optimistic policy for effective exploration. Modules will be added to it in the order they are passed in the constructor. We propose an on-line algorithm for learning in such parameterized models, called the Parameter Elimination (PEL) algorithm, and analyze its performance in terms of the the total mistake bound criterion, which upper-bounds the total number of suboptimal actions performed by the algorithm over the infinite time horizon. We consider reinforcement learning in a parameterized setup, where the controlled model is known to belong to a finite set of Markov Decision Processes (MDPs) under the discounted return criteria. ELIMINATE DELAY BY MITIGATION / ACCELERATION SHAIKH ASIF ABDUS SAEED PROJECT MANAGEMENT PROGRAMME INSTITUTION OF BUSINESS AUGUST 2009 DISSERTATION SUPERVISOR : Dr. ![]()
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