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 

class
samplers.evaluate.
evaluate
(info_sampler, model, output=None, packages_path=None, name=None)¶ 
initialize
()¶ Creates a 1point collection to store the point at which the posterior is evaluated.

products
()¶ Auxiliary function to define what should be returned in a scripted call.
Returns: The sample Collection
containing the sequentially discarded live points.
