This unit provides key tools for the statistical and numerical analysis of complex problems in physics and astrophysics. It includes a solid introduction to the R statistical programming language and contains many practical examples, mainly from cosmology and astrophysics, but extending to general physics and medicine.
Learning Outcomes
Students are able to (1) use R, interface with other scientific languages and I/O scientific data; (2) demonstrate mastery essential numerical analysis techniques in R and benefit from various packages to quickly solve numerical problems.; (3) explain basic statistical concepts (probability trees, joint probabilities, ensembles, resampling, frequentist statistics, tests, correlations, distributions, expectations, moments); (4) apply Bayesian inference to a wide range of scientific problems and master related concepts (maximum likelihood, Fisher information, Laplace approximation, Jeffreys prior); (5) demonstrate understanding of advanced inference problems and packages available to solve them; including by MCMC algorithms.; and (6) demonstrate mastery temporal and spatial statistical estimators and their Fourier space representations.
Assessment
Indicative assessments in this unit are as follows: (1) programming assignments and (2) written assignments. Further information is available in the unit outline.
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