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Fitting (special modeling). 预习 BR2003, Chap. 7. 董小波 2009.11.25. 最大似然原理下的最小二乘法. The Two premises of ML LS (particularly the Gaussian-distribution assumption) Accounting for the measurement uncertainties (1) in the independent variables (2) in both axes The implementation
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Fitting (special modeling) 预习BR2003, Chap. 7 董小波 2009.11.25
最大似然原理下的最小二乘法 • The Two premises of MLLS (particularly the Gaussian-distribution assumption) • Accounting for the measurement uncertainties (1) in the independent variables (2) in both axes • The implementation [see svdfit.pro in IDL]
The essence of Chap.7 • What is linear model? Eqns. 7.3, -- 7.7 • Other minor points: For orthogonal polynomials, Legendre Poly., it’s ok to skim through them. Only see the reference of svdfit.pro. For the linearization of nonlinear functions, see Fig. 7.3, with particular notice on measurement errors.
Case discussion Today http://ustcastroph.blog.sohu.com/#tp_95a1b1f5a7a Case for Nov. 25 class 现有一个类星体光谱观测样本,红移在0.45—0.8之间,共2092个源。根据光谱,我们可以测得MgII λ2800A发射线的半高全宽(FWHM, 单位km/s),光度(L_mgii)和等值宽度(EW);可以测得类星体连续谱在3000A处的光度[ L_3000= 3000A * L_lambda(3000); 单位erg/s ]。数据见data_for_fitting_case.txt.zip文件 (ftp://210.45.66.48/teaching/methods09/Course_Notes_and_Homeworks/fitting/)。 请通过拟合拟合,得到logL_mgii 和 logL_3000 之间的关系。 (1),logL_3000作为自变量X, logL_mgii作为因变量Y; 两者均不考虑误差。 (2), logL_mgii作为X, logL_3000作为Y; 两者均不考虑误差。 (3), logL_3000作为X, logL_mgii作为Y; 考虑Y的测量误差。 (4), logL_mgii作为X, logL_3000作为Y; 考虑Y的测量误差。 (5, optional), logL_3000作为自变量X, logL_mgii作为因变量Y; 考虑X和Y的测量误差。 Tips: i. 可以登陆到CfA的QSO server (85.199), 使用IDL等软件编程。可利用等svdfit.pro, mpfit软件包,等。 ii. 对于选作的(5),可以参考Press et al. book, Section 15.3; 参考IDL code, fitexy.pro 。
For the next class • Read in advance chapters 8 and 9. • Pay much time to finish the case, see: ftp://210.45.66.48/teaching/methods09/Course_Notes_and_Homeworks/fitting/ or http://ustcastroph.blog.sohu.com/ Advice: practice is more important than reading.