pymc.logp — PyMC 5.22.0 documentation (original) (raw)

pymc.logp(rv, value, warn_rvs=None, **kwargs)[source]#

Create a graph for the log-probability of a random variable.

Parameters:

rvTensorVariable

valuetensor_like

Should be the same type (shape and dtype) as the rv.

warn_rvsbool, default True

Warn if RVs were found in the logp graph. This can happen when a variable has other other random variables as inputs. In that case, those random variables should be replaced by their respective values.pymc.logprob.conditional_logp can also be used as an alternative.

Returns:

logpTensorVariable

Raises:

RuntimeError

If the logp cannot be derived.

Examples

Create a compiled function that evaluates the logp of a variable

import pymc as pm import pytensor.tensor as pt

mu = pt.scalar("mu") rv = pm.Normal.dist(mu, 1.0)

value = pt.scalar("value") rv_logp = pm.logp(rv, value)

Use .eval() for debugging

print(rv_logp.eval({value: 0.9, mu: 0.0})) # -1.32393853

Compile a function for repeated evaluations

rv_logp_fn = pm.compile_pymc([value, mu], rv_logp) print(rv_logp_fn(value=0.9, mu=0.0)) # -1.32393853

Derive the graph for a transformation of a RandomVariable

import pymc as pm import pytensor.tensor as pt

mu = pt.scalar("mu") rv = pm.Normal.dist(mu, 1.0) exp_rv = pt.exp(rv)

value = pt.scalar("value") exp_rv_logp = pm.logp(exp_rv, value)

Use .eval() for debugging

print(exp_rv_logp.eval({value: 0.9, mu: 0.0})) # -0.81912844

Compile a function for repeated evaluations

exp_rv_logp_fn = pm.compile_pymc([value, mu], exp_rv_logp) print(exp_rv_logp_fn(value=0.9, mu=0.0)) # -0.81912844

Define a CustomDist logp

import pymc as pm import pytensor.tensor as pt

def normal_logp(value, mu, sigma): return pm.logp(pm.Normal.dist(mu, sigma), value)

with pm.Model() as model: mu = pm.Normal("mu") sigma = pm.HalfNormal("sigma") pm.CustomDist("x", mu, sigma, logp=normal_logp)