# How to use logp within a model

**URL:** <https://discourse.pymc.io/t/how-to-use-logp-within-a-model/4320>\
**Category:** Questions\
**Created:** [January 9, 2020, 2:34am UTC](https://discourse.pymc.io/t/how-to-use-logp-within-a-model/4320 "2020-01-09T02:34:38Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Paul\_Scotti](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/paul_scotti/32/2139_2.png) [@Paul\_Scotti](https://discourse.pymc.io/u/Paul_Scotti)\
**Post date:** [January 9, 2020, 2:34am UTC](https://discourse.pymc.io/t/how-to-use-logp-within-a-model/4320/1 "2020-01-09T02:34:38Z")

</div>

Is there any way to accomplish a model that works like the following?

```
with pm.Model():
    a = pm.Uniform('a', lower=-np.pi, upper=np.pi)
    x = pm.VonMises('x', mu=a, kappa=33.3, shape=50)
    y = pm.Deterministic('y', logp({'x[id]': features}), observed=z) 

```

I have 50 von Mises distributions and I want to calculate the probability in each von Mises for each value of the variable “features”. E.g., for the first von Mises distribution, x[0], I want to compute the probability (y-axis value) for each given x-axis value (given by the variable “features”). I tried to do this via logp but I may be totally wrong in using this approach…

variable legend:  
° features = 1000 values ranging from -np.pi to np.pi (if converted from radians to degrees, it would range from 0 to 360 degrees)  
° id = 1000 values ranging from 0 to 49 (i.e., each “feature” has an associated id)  
° z = 1000 observed values ranging from 0 to 1  
° x[id] refers to each of the 50 von Mises distributions