Titlebook: Probability and Statistics in Experimental Physics; Byron P. Roe Textbook 19921st edition Springer-Verlag New York 1992 Monte Carlo method

colloquial

Methods for Estimating Parameters. Least Squares and Maximum Likelihood, developed for these problems and, in many cases, the estimation process can be automated and turned into almost a crank-turning operation. Nonetheless, as we will see, it is very important to understand in detail what we are doing.

等待

Curve Fitting,urn the procedure into a crank-turning procedure. If the dependence on the parameters is intrinsically non-linear, we will see that the problem is much harder, but general computer programs to find minima of multidimensional functions can be of considerable help.

Rinne-Test

Bartlett , Function; Estimating Likelihood Ratios Needed for an Experiment,also it is sometimes hard to interpret the non-gaussian errors which result. The use of the Bartlett . function is a technique to introduce new variables to make the distribution function closer to normal.

NIP

FRET

Beyond Maximum Likelihood and Least Squares; Robust Methods, of the points on a curve, for example, are not normal and have long tails, then, as we noted in Chapter 13, estimates of goodness of fit may be seriously biased. The tests discussed in this chapter tend to be robust, with results which are independent of the particular distribution being tested.

極大痛苦

https://doi.org/10.1007/978-1-4757-2186-7Monte Carlo method; Parameter; experiment; experimental physics; normal distribution; statistics