`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/latest/cobaya/samplers/evaluate/evaluate.py’>