Colloquium/Seminar
Year | Month |
2018 | Jan Feb Mar Apr May Jun Jul Aug |
2017 | Jan Feb Mar Apr May Jun Jul Aug Oct Nov Dec |
2016 | Jan Feb Mar Apr May Jun Jul Aug Oct Nov Dec |
2015 | Jan Feb Mar Apr May Jun Aug Sep Oct Nov Dec |
2014 | Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec |
2013 | Jan Feb Mar Apr May Jun Aug Sep Nov Dec |
2012 | Jan Feb Apr May Jun Jul Aug Sep Nov Dec |
2011 | Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec |
2010 | Jan Feb Mar Apr May Jun Sep Oct Nov Dec |
2009 | Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec |
2008 | Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec |
2007 | Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec |
2006 | Jan Feb Mar Apr May Jun Jul Sep Oct Nov Dec |
2005 | Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec |
2004 | Jan Feb Mar Apr May Aug Sep Oct Nov Dec |
Event(s) on October 2008
- Monday, 6th October, 2008
Title: Market Efficiency and Investment Strategies, a case study of Hong Kong racetrack betting Market Speaker: Prof. Gu, Ming-Gao , Department of Statistics , The Chinese University of Hong Kong, HKSAR, China Time/Place: 11:30 - 12:30
NAB209, Dr. Wu Lee Sun Lecture Theatre, Lam Woo International Conference Centre,Abstract: We review the definitions of efficient market hypothesis and the basics of horse racing betting markets. Different ranking data models have been used for predictions in horse racing. Problems involved here are partially ranked data, model selections, variable selections and predictions. For computation, MCMC techniques can be applied. Constant rebalanced portfolio investment strategies can be used in actual investment. Hong Kong Jockey Club horse racing data were used to show that the horse racing betting markets are weakly efficient but not strongly efficient. - Wednesday, 8th October, 2008
Title: Institute for Computational Mathematics (ICM) Lecture Series: Matrices, Moments and Quadrature (Lecture1) Speaker: Prof. Gerard Meurant, Commissariat a l`Energie Atomique, CEA/DIF, France Time/Place: 14:00 - 15:30
FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist UniversityAbstract: The aim of this series of lectures is to describe and explain the beautiful mathematical relationships between matrices, moments, orthogonal polynomials, quadrature rules and the Lanczos and conjugate gradient algorithms. The main topic is to obtain numerical methods to estimate or in some cases to bound quantities like I[f] = uT f (A)v where u and v are given vectors, A is a symmetric nonsingular matrix and f is a smooth function. There are many instances in which one would like to compute bilinear forms like uT f (A)v. A first application is the computation of some elements of the matrix f (A) when it is not desired or feasible to compute all of f (A). Computation of quadratic forms rTA−ir for i = 1, 2 is interesting to obtain estimates of error norms when one has an approximate solution of a linear system Ax = b and r is the residual vector b − Ax. Bilinear or quadratic forms arise naturally for the computation of parameters in problems like least squares, total least squares and regularization methods for solving ill–posed problems. We will describe the algorithms and give some examples of applications. The contents of the lectures are the following. 1. Orthogonal polynomials and properties of tridiagonal matrices. 2. The Lanczos and conjugate gradient (CG) algorithms and computation of Jacobi matrices 3. Gauss quadrature and bounds for bilinear forms uT f (A)v 4. Applications: Bounds for elements of f (A), Estimates of error norms in CG, Least squares and total least squares, Discrete ill–posed problems. - Friday, 10th October, 2008
Title: Institute for Computational Mathematics (ICM) Lecture Series: Matrices, Moments and Quadrature (Lecture 2) Speaker: Prof. Gerard Meurant, Commissariat a l`Energie Atomique, CEA/DIF, France Time/Place: 14:00 - 15:30
FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist UniversityAbstract: The aim of this series of lectures is to describe and explain the beautiful mathematical relationships between matrices, moments, orthogonal polynomials, quadrature rules and the Lanczos and conjugate gradient algorithms. The main topic is to obtain numerical methods to estimate or in some cases to bound quantities like I[f] = uT f (A)v where u and v are given vectors, A is a symmetric nonsingular matrix and f is a smooth function. There are many instances in which one would like to compute bilinear forms like uT f (A)v. A first application is the computation of some elements of the matrix f (A) when it is not desired or feasible to compute all of f (A). Computation of quadratic forms rTA−ir for i = 1, 2 is interesting to obtain estimates of error norms when one has an approximate solution of a linear system Ax = b and r is the residual vector b − Ax. Bilinear or quadratic forms arise naturally for the computation of parameters in problems like least squares, total least squares and regularization methods for solving ill–posed problems. We will describe the algorithms and give some examples of applications. The contents of the lectures are the following. 1. Orthogonal polynomials and properties of tridiagonal matrices. 2. The Lanczos and conjugate gradient (CG) algorithms and computation of Jacobi matrices 3. Gauss quadrature and bounds for bilinear forms uT f (A)v 4. Applications: Bounds for elements of f (A), Estimates of error norms in CG, Least squares and total least squares, Discrete ill–posed problems. - Tuesday, 14th October, 2008
Title: Bayesian Data Integration for Periodicity Detection on Cell Cycle Gene Expression Data Speaker: Dr. Fan Xiaodan, Statistics Department, The Chinese University of Hong Kong, Hong Kong Time/Place: 11:30 - 12:30
FSC1217, Fong Shu Chuen Library, HSH Campus,Abstract: Periodicity detection is important for cell cycle study, which is the key to understanding some serious diseases such as cancer. A lot of experimental effort has been devoted to improve the discrimination power of periodicity detection, but the computational side still needs improvement to fully explore the discrimination power provided by these data sets. I will present a Bayesian data integration approach for combining multiple microarray time-course data sets to detect periodically expressed genes. The result is striking for the cell-cycle research community. It also shows that the power and potential of model-based Bayesian meta-analysis is appealing. The main focus will put on probabilistic modeling of the cell cycle data and Bayesian computation of the whole system. Little biology background is needed for understanding the key point of the talk. - Wednesday, 15th October, 2008
Title: Institute for Computational Mathematics (ICM) Lecture Series: Matrices, Moments and Quadrature (Lecture 3) Speaker: Prof. Gerard Meurant, Commissariat a l`Energie Atomique, CEA/DIF, France Time/Place: 14:00 - 15:30
FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist UniversityAbstract: The aim of this series of lectures is to describe and explain the beautiful mathematical relationships between matrices, moments, orthogonal polynomials, quadrature rules and the Lanczos and conjugate gradient algorithms. The main topic is to obtain numerical methods to estimate or in some cases to bound quantities like I[f] = uT f (A)v where u and v are given vectors, A is a symmetric nonsingular matrix and f is a smooth function. There are many instances in which one would like to compute bilinear forms like uT f (A)v. A first application is the computation of some elements of the matrix f (A) when it is not desired or feasible to compute all of f (A). Computation of quadratic forms rTA−ir for i = 1, 2 is interesting to obtain estimates of error norms when one has an approximate solution of a linear system Ax = b and r is the residual vector b − Ax. Bilinear or quadratic forms arise naturally for the computation of parameters in problems like least squares, total least squares and regularization methods for solving ill–posed problems. We will describe the algorithms and give some examples of applications. The contents of the lectures are the following. 1. Orthogonal polynomials and properties of tridiagonal matrices. 2. The Lanczos and conjugate gradient (CG) algorithms and computation of Jacobi matrices 3. Gauss quadrature and bounds for bilinear forms uT f (A)v 4. Applications: Bounds for elements of f (A), Estimates of error norms in CG, Least squares and total least squares, Discrete ill–posed problems. - Friday, 17th October, 2008
Title: Institute for Computational Mathematics (ICM) Lecture Series: Matrices, Moments and Quadrature (Lecture 4) Speaker: Prof. Gerard Meurant, Commissariat a l`Energie Atomique, CEA/DIF, France Time/Place: 14:00 - 15:30
FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist UniversityAbstract: The aim of this series of lectures is to describe and explain the beautiful mathematical relationships between matrices, moments, orthogonal polynomials, quadrature rules and the Lanczos and conjugate gradient algorithms. The main topic is to obtain numerical methods to estimate or in some cases to bound quantities like I[f] = uT f (A)v where u and v are given vectors, A is a symmetric nonsingular matrix and f is a smooth function. There are many instances in which one would like to compute bilinear forms like uT f (A)v. A first application is the computation of some elements of the matrix f (A) when it is not desired or feasible to compute all of f (A). Computation of quadratic forms rTA−ir for i = 1, 2 is interesting to obtain estimates of error norms when one has an approximate solution of a linear system Ax = b and r is the residual vector b − Ax. Bilinear or quadratic forms arise naturally for the computation of parameters in problems like least squares, total least squares and regularization methods for solving ill–posed problems. We will describe the algorithms and give some examples of applications. The contents of the lectures are the following. 1. Orthogonal polynomials and properties of tridiagonal matrices. 2. The Lanczos and conjugate gradient (CG) algorithms and computation of Jacobi matrices 3. Gauss quadrature and bounds for bilinear forms uT f (A)v 4. Applications: Bounds for elements of f (A), Estimates of error norms in CG, Least squares and total least squares, Discrete ill–posed problems.