http://dr.lib.sjp.ac.lk/handle/123456789/1899 2026-01-07T04:01:44Z http://dr.lib.sjp.ac.lk/handle/123456789/8505 Bayesiantreatmentofnon-standardproblemsintestanalysis Silva, Rajitha M.; Guan, Yuping; Swartz, Tlm B. This paper extends the methods of [10] in an attempt to handle non-standard problems in test analysis. The approach is based on a Bayesian framework where test characteristics are treated as random parameters for which posterior probability assessments are available. The generality of the approach permits straightforward analyses of problems that may be difficult using standard classical test theory and standard item response theory. We first illustrate the methods on aviation test scores where the test outcomes are not dichotomous (i.e. correct and incorrect responses). Instead, the approach is modified to handle questions with answers on a five-point ordinal scale. The second problem addresses the complication of the assessment of instructors in addition to the assessment of test questions and students. 2019-01-01T00:00:00Z http://dr.lib.sjp.ac.lk/handle/123456789/8404 Development of a rapid, sensitive and specific DNA-based method to detect Ralstonia solanacearum in potato for quarantine purposes Perera, A.A.U.; Weerasena, O.V.D.S.J.; Dasanayaka, P.N.; Wickramarachchi, D.C. 2018-06-30T00:00:00Z http://dr.lib.sjp.ac.lk/handle/123456789/6980 Performance Optimized Expectation Conditional Maximization Algorithms for Nonhomogeneous Poisson Process Software Reliability Models Jayasinghe, C.L. Attached; nhomogeneous Poisson process (NHPP) and software reliability growth models (SRGM) are a popular approach to estimate useful metrics such as the number of faults remaining, failure rate, and reliability, which is defined as the probability of failure free operation in a specified environment for a specified period of time. We propose performance-optimized expectation conditional maximization (ECM) algorithms for NHl)P SRGM. In contrast to the expectation maximization (EM) algorithm, the ECM algorithm reduces the maximum-likelihood estimation process to multiple simpler conditional maximization (CM)-steps. The advantage of these CM-steps is that they only need to consider one variable at a time, enabling implicit solutions to update rules when a closed form equation is not available for a model parameter. We compare the performance of our ECM algorithms to previous EM and ECM algorithms on many datasets from the research literature. Our results indicate that our ECM algorithms achieve two orders of magnitude speed up over the EM and ECM algorithms of [11 when their experimental methodology is considered and three orders of magnitude when knowledge of the maximum-likelihood estimation is removed, whereas our approach is as much as 60 times faster than the EM algorithms of [2]. We subsequently propose a two-stage algorithm to further accelerate performance. 2017-09-01T00:00:00Z http://dr.lib.sjp.ac.lk/handle/123456789/6979 Maximum-Likelihood Estimation of Parameters of NHPP Software Reliability Models Using Expectation Conditional Maximization Algorithm Jayasinghe, C.L. Attached; ince its introduction in 1977, the expectation maximization (EM) algorithm has been one of the most important and widely used estimation method in estimating parameters of distributions in the presence of incomplete information. In this paper, a variant of the EM algorithm, the expectation conditional maximization (ECM) algorithm, is introduced for the first time and it provides a promising alternative in estimating the parameters of nonhomogeneous poisson (NHPP) software reliability growth models (SRGM). This algorithm circumvents the difficult M-step of the EM algorithm by replacing it by a series of conditional maximization steps. The utility of the ECM approach is demonstrated in the estimation of parameters of several well-known models for both time domain and time interval software failure data. Numerical examples with real-data indicate that the ECM algorithm performs well in estimating parameters ofNHPP SRGM with complex mean value functions and can produce a faster rate of convergence. 2016-09-01T00:00:00Z