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Bayesian Methods
Subsections
- Introduction
- Priors
- Why priors matter, and when they don't
- Available prior families
- Practical advice
- Joint priors: the smoothness regularizer
- Samplers
- Metropolis–Hastings (chain type mh)
- Goodman–Weare (chain type gw)
- Hamiltonian Monte Carlo (hmc)
- Nested sampling (nest)
- A worked example: a simulated Chandra spectrum
- Setting up the data and finding the MLE
- Running each sampler
- Comparing the results
- When to pick what, for this kind of problem
- Choosing a sampler
- Model comparison by evidence
- Convergence diagnostics
- Common pitfalls
- Further reading