Thesis--Bonn. Thesis statement on added t.p. / Includes bibliographical references (p. 53-57).

"The purpose of this paper will be to develop a semi-automatic process for numerical solution of ordinary differential equations, associated commonly with the names of Runge and Kutta, which by its essential features can be characterized as an iterative 'method of successive substitutions'"--Introduction. / Typescript. / "August, 1959." / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: H. C. Griffith, Professor Directing Paper. / Includes bibliographical references (leaf 48).

Call number: LD2668 .T4 1978 G92 / Master of Science

O FBST (Full Bayesian Significance Test) é um procedimento para testar hipóteses precisas, apresentado por Pereira e Stern (1999), e baseado no cálculo da probabilidade posterior do conjunto tangente ao conjunto que define a hipótese nula. Este procedimento é uma alternativa Bayesiana aos testes de significância usuais. Neste trabalho, estudamos a relação entre os resultados do FBST e de um teste freqüentista, o TRVG (Teste da Razão de Verossimilhanças Generalizado), através de alguns problemas clássicos de testes de hipóteses. Apresentamos, também, todos os procedimentos computacionais utilizados para a resolução automática dos dois testes para grandes amostras, necessária ao estudo da relação entre os testes. / FBST (Full Bayesian Significance Test) is a procedure to test precise hypotheses, presented by Pereira and Stern (1999), which is based on the calculus of the posterior probability of the set tangent to the set that defines the null hypothesis. This procedure is a Bayesian alternative to the usual significance tests. In the present work we study the relation between the FBST\'s results and those of a frequentist test, GLRT (Generalised Likelihood Ratio Test) through some classical problems in hypotesis testing. We also present all computer procedures that compose the automatic solutions for applying FBST and GLRT on big samples what was necessary for studying the relation between both tests.

by Sze Tsz-leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 91). / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Basic concepts of Monte Carlo integration --- p.1 / Chapter 1.1.1 --- Importance sampling --- p.4 / Chapter 1.1.2 --- Control variate --- p.5 / Chapter 1.1.3 --- Antithetic variate --- p.6 / Chapter 1.1.4 --- Stratified sampling --- p.7 / Chapter 1.1.5 --- Biased Estimator --- p.10 / Chapter 1.2 --- Some special methods in Monte Carlo integration --- p.11 / Chapter 1.2.1 --- Haber´ةs modified Monte Carlo quadrature I --- p.11 / Chapter 1.2.2 --- Haber's modified Monte Carlo quadrature II --- p.11 / Chapter 1.2.3 --- Weighted Monte Carlo integration --- p.12 / Chapter 1.2.4 --- Adaptive importance sampling --- p.13 / Chapter Chapter 2 --- New methods / Chapter 2.1 --- The use of Newton Cotes quadrature formulae in stage one --- p.17 / Chapter 2.1.1 --- Using one-dimensional trapezoidal rule --- p.17 / Chapter 2.1.2 --- Using two-dimensional or higher dimensional product trapezoidal rule --- p.21 / Chapter 2.1.3 --- Extension to higher order one-dimensional Newton Cotes formulae --- p.32 / Chapter 2.2 --- The use of Guass quadrature rule in stage one --- p.45 / Chapter 2.3 --- Some variations of the new methods --- p.56 / Chapter 2.3.1 --- Using probability points in both stages --- p.56 / Chapter 2.3.2 --- Importance sampling --- p.59 / Chapter 2.3.2.1 --- Triangular distribution --- p.60 / Chapter 2.3.2.2 --- Beta distribution --- p.64 / Chapter Chapter 3 --- Examples / Chapter 3.1 --- Example one: using trapezoidal rule as basic rule --- p.73 / Chapter 3.1.1 --- One-dimensional case --- p.73 / Chapter 3.1.2 --- Two-dimensional case --- p.80 / Chapter 3.2 --- Example two: Using Simpson's 3/8 rule as basic rule --- p.85 / Chapter 3.3 --- Example three： Using Guass rule as basic rule --- p.86 / Chapter Chapter 4 --- Conclusion and discussions --- p.88 / Reference --- p.91

Heung Ling-lung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 81-82). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.iv / Chapter Chapter1: --- Introduction --- p.1 / Chapter Chapter2: --- Review of binomial schemes and trinomial schemes --- p.4 / Chapter Chapter3: --- Binomial/trinomial scheme from the viewpoint of quadrature --- p.12 / Chapter Chapter4: --- Binomial/trinomial schemes from Gaussian quadrature formula --- p.16 / Chapter Chapter5: --- New Schemes from other quadrature formula --- p.27 / Chapter Chapter6: --- Multinomial scheme --- p.35 / Chapter Chapter7: --- Numerical results --- p.41 / Chapter Chapter8: --- Conclusion --- p.47 / Appendix --- p.49 / Bibliography --- p.81

No description available.

O FBST (Full Bayesian Significance Test) é um procedimento para testar hipóteses precisas, apresentado por Pereira e Stern (1999), e baseado no cálculo da probabilidade posterior do conjunto tangente ao conjunto que define a hipótese nula. Este procedimento é uma alternativa Bayesiana aos testes de significância usuais. Neste trabalho, estudamos a relação entre os resultados do FBST e de um teste freqüentista, o TRVG (Teste da Razão de Verossimilhanças Generalizado), através de alguns problemas clássicos de testes de hipóteses. Apresentamos, também, todos os procedimentos computacionais utilizados para a resolução automática dos dois testes para grandes amostras, necessária ao estudo da relação entre os testes. / FBST (Full Bayesian Significance Test) is a procedure to test precise hypotheses, presented by Pereira and Stern (1999), which is based on the calculus of the posterior probability of the set tangent to the set that defines the null hypothesis. This procedure is a Bayesian alternative to the usual significance tests. In the present work we study the relation between the FBST\'s results and those of a frequentist test, GLRT (Generalised Likelihood Ratio Test) through some classical problems in hypotesis testing. We also present all computer procedures that compose the automatic solutions for applying FBST and GLRT on big samples what was necessary for studying the relation between both tests.

No description available.

No description available.