Statistical methods

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This lecture will be a primer on data modelling and inference, covering

  • basic probabilistic description of data and models
  • assigning probability distributions: likelihoods and priors
  • options for characterising posterior pdfs:
    • Brute force
    • Laplace approximation
    • MCMC
  • model checking and comparison: goodness of fit, evidence

Worked examples will include simple galaxy-scale lens models, with extended and point sources, and extensions to include more effects.

Slides (PDF)

Questions? Email me at dr.phil.marshall at gmail.com

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