evaluate sampler

This is a dummy sampler that just evaluates the likelihood at a reference point. You can use it to test your likelihoods (take a look too at the model wrapper for a similar but more interactive tool).

To use it, simply make the sampler block:

sampler:
  evaluate:
    # Optional: override parameter values
    override:
      # param: value

The posterior will be evaluated at a point sampled from the reference pdf (which may be a fixed value) or from the prior if there is no reference. Values passed through evaluate:override will take precedence. For example:

params:
  a:
    prior:
      min: -1
      max:  1
    ref: 0.5
  b:
    prior:
      min: -1
      max:  1
    ref:
      dist: norm
      loc: 0
      scale: 0.1
  c:
    prior:
      min: -1
      max:  1
  d:
    prior:
      min: -1
      max:  1
    ref: 0.4

sampler:
  evaluate:
    override:
      d: 0.2

In this case, the posterior will be evaluated for each parameter at:

a: Exactly at \(0.5\).

b: Sampled from the reference pdf: a Gaussian centred at \(0\) with standard deviation \(0.1\).

c: From the prior, since there is no reference pdf: sampled uniformly in the interval \([-1, 1]\).

d: From the override, which takes precedence above all else.

Note

If using this sampler cobaya appears to be stuck, this normally means that it cannot sample a point with finite posterior value. Check that your prior/likelihood definitions leave room for some finite posterior density, e.g. don’t define an external prior that imposes that \(x>2\) if the range allowed for \(x\) is just \([0,1]\).

Evaluate sampler class

Synopsis:

Dummy “sampler”: simply evaluates the likelihood.

Author:

Jesus Torrado

samplers.evaluate.evaluate

alias of <module ‘samplers.evaluate.evaluate’ from ‘/home/docs/checkouts/readthedocs.org/user_builds/cobaya/checkouts/stable/cobaya/samplers/evaluate/evaluate.py’>