Lectures for Physics Laboratory
- Lecture 1 (13 April)
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- Lecture 2 (20 April)
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- Lecture 3 (27 April)
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- Lecture 4 (4 May)
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- Slides: PhysLab_04.pdf
- MCMC — theory and practice (left to self study):
- For an introduction to MCMC, Metropolis and Gibbs sampler:
C. Andrieu et al., An introduction to MCMC for Machine Learning,
Machine Learning, 50 (2003) 5-43,
https://doi.org/10.1023/A:1020281327116
- JAGS (Just Another Biggs Sampler)
- Install JAGS
and rjags (free and multiplatform).
(Those who use Python might want to use pyjags)
- Some simple examples (just run the script to check that everything is well installed)
- simple_simulations.R
(JAGS `improperly' used as random generator,
to generate quantities following normal, binomial, Poisson and
exponential distributions)
- Please install also the R package
coda.
(In order to check that it is installed use the R command library(coda):
if you get no warning message it is ok)
- Several examples of simple applications of Physics interest
can be found here
- Examples related to this lecture (also with alternatives)
[Note: the R code can be taken as pseudocode for those who
use Python]
- Lecture 5 (11 May)
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- Lecture 6 (18 May)
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