Applied Statistics - Week 3

The following is a description of what we will go through during this week of the course. The chapter references and computer exercises are considered read, understood, and solved by the beginning of the following class, where I'll shortly go through the exercise solution.

General notes, links, and comments:

Particle Data Group (PDG) note on Monte Carlo.

Wiki transformation method.

Wiki Hit-and-Miss (Von Neumann) method.

Monday:
We will consider Monte Carlo Techniques, which is a ubiquitious tool in statistics. The central point is to be able to generate random numbers according to a given distribution, and subsequently use this.

Reading:

Glen Cowan: Chapter 3.
Lecture(s):

Monte Carlo methods.
Computer Exercise(s):

Making Random Numbers: MakingRandomNumbers.py

TransHitMiss: TransHitMiss.py

Tuesday:
The main theme will be the Likelihood function, and the central role it plays in statistics. It is in principle the most powerful method for fitting, and estimation and ChiSquare can be derived from it.

Reading:

Barlow, chapter 5.3 to 5.7 (but not 5.5 and the proofs).
Lecture(s):

Likelihood function
Computer Exercise(s):

LikelihoodFit: LikelihoodFit.py

Friday:
I will do a summary of the curriculum discussed so far. This will also allow you to ask questions about all exercises up till now. Also, I'll give an introduction to the problem set. Finally, we will have some fun with a small little exercise, illustrating the use of random numbers and simulation.

Reading:

Barlow, in which you should by now have read chapters 1-6!
Lecture(s):

Summary of curriculum so far.
Computer Exercise(s):

Pi Estimate: PiEstimate.py

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Last updated: 29th of November 2015.