Information
 Talks can be given in English or German
 Structure of the presentation:
 ~2030 mins Talk with slides (I'll bring laptop and projector; PDF files will work, everything else has to be tested beforehand)
 ~1020 mins Visualisation in a simple simulation program + code presentation
 ~1020 mins Discussion
 It might be useful to recycle (parts of) the simulation programs used in previous talks if they were in the same programming language, e.g. if one wants to compare one approach to another. Feel free to collaborate on the programming projects where that seems helpful.
 If you need discussion, or want help finding/understanding the literature on your subject, or have specific questions, (at least one of these will usually be the case!), then please come and see me at least 23 weeks before your talk is scheduled.
 In any case I would like to briefly discuss your almost finalised slides/program ~one week before your talk (possibly after the previous talk if the room is still available).
General references
Topics and dates
Here you can find the listing and schedule of the topics we distributed in the first week. I have added a few bullet points for each topic to give an idea of the content I had in mind. Also, I have indicated ideas for the simple example programs. Please use these only as suggestions and if you have other ideas feel free to bring them up!
21.10. Frank Siegert:
Applications of MonteCarlo methods in particle physics
28.10. David Meisel:
Random numbers
 Classification: random vs. pseudorandom vs. quasirandom
 Criteria for pseudorandom numbers
 Algorithms
 Example program: Implementation of a few algorithms and test of their properties, comparison to builtin random number generators



4.11. Max Hils:
Without MonteCarlo methods: integration by numerical quadrature
 Classification of algorithms
 Error estimate and its scaling with the number of dimensions
 Example program: Implement a few algorithms and compare their results for an analytically known 1D integral with the expected error estimates with fixed number of evaluations
 Reference: Computational Physics http://www.compphys.tudresden.de/cp2013/



11.11. Lukas Schröder:
MonteCarlo integration basics
 Motivation and basic idea
 Law of large numbers
 Error estimate
 Example program: Volume of a ddimensional sphere in MC vs. (simple) classical integration. Estimate the (statistical) uncertainty of each result. Compare the runtime (= number of points N) necessary to reach a given accuracy target (e.g. 1%). Plot the scaling of the MC result with N for some fixed values of d. Discuss the scaling with d.



18.11. Fabian Heisse:
Improving MonteCarlo integration: Stratified sampling
 Motivation
 Introduction to stratified sampling
 Example program: Demonstrate improvement from stratified sampling for different suitably chosen (1D?) integrals



18.11. Christian Bartzsch:
Improving MonteCarlo integration: Importance sampling
 Motivation
 Introduction to importance sampling
 Example program: Demonstrate improvement from importance sampling for different suitably chosen (1D?) integrals



2.12. Jonas Golde:
MonteCarlo integration: Adaptive methods (VEGAS)
 Motivation and basic idea
 Algorithm in 1D and nD
 Limitations/assumptions for efficient usage
 Relations to importance sampling
 Example program: VEGAS implementation to integrate spontaneously selected 1Dfunctions
 References:



9.12. Johannes Krause:
MonteCarlo integration with the multichannel method
 Motivation and basic idea
 Automatic adaptation of weights
 Example program: Demonstrate improvement from a multichannel for a suitably chosen integral (e.g. with doublepeak structure)
 Original reference: http://arxiv.org/abs/hepph/9405257



16.12. Philipp Horn:
MonteCarlo sampling basics
 Motivation
 Inverse transform method
 Hitormiss method
 Example program: reproduce a given distribution function using inversetransform and hitormiss including a comparison of different hitormiss input distributions



13.1. Martin Rehwald:
Particle collision phase space with the RAMBO algorithm
 Phase space for n massless particles
 RAMBO algorithm
 Example program: Generate 10000 events for a 2>4 scattering of massless particles, with a fixed configuration of incoming momenta of p_{0,1}=(E, px, py, pz)=(1000, 0, 0, +1000) and a uniform distribution of the outgoing momenta. Plot their distribution in the theta variable. (Display some of them in 3D. ?)
 Original reference: http://dx.doi.org/10.1016/00104655(86)901190 (only from TUD)



20.1. Alexander Melzer:
Sampling generalised radioactive decay type distributions
 Motivation
 Parton branching probabilities and the analogy to radioactive decay
 Veto algorithm
 Example program: Simplified parton cascade: One splitting function, simplified splitting kernel
 Reference: http://arxiv.org/abs/hepph/0603175 Section 4, in particular 4.2



27.1. Robert Wolff:
MonteCarlo sampling with the Metropolis/Hastings algorithm
 Introduction to Metropolis algorithm, Markov chains
 Extension to arbitrary distribution functions (MetropolisHastings)
 MarkovChainMonteCarlo (MCMC) methods
 Example program:
 References:


