Overview. We have a group of persons (eg `2`

). Each person performs a different number of experiment `n`

(eg `10, 20`

) and we record `k`

, ie how many times the experiment was a success (eg `2, 3`

). For each person, we have have some information like age (eg `22, 34`

).

I’d like to estimate the success rate (`theta`

) and how this is affected by the `age`

. This is the model I’ve implemented:

```
model = pm.Model()
n = [10, 20]
k = [2, 3]
age = [22, 34]
with model:
intercept = pm.Normal('intercept', mu=0, sigma=10)
slope = pm.Normal('slope', mu=0, sigma=100)
y = intercept + slope * age
theta = pm.Deterministic('theta', pm.math.sigmoid(y))
binominal = pm.Binomial('binominal', n=n, p=theta, observed=k)
p_trace = pm.sample(50, init='adapt_diag')
```

It is modeled as a Binomial distribution for the experiments where the success rate is a logistic regression with the age as an input.

The trace of `theta`

has a number of columns equal to the number of observations (2).

```
p_trace['theta'].shape
(100, 2)
```

Why? In this toy example, I set just 2 observations, but they are way more in reality. I cannot analyze the traceplot (too many plots). How can I model the above by then having just **one** theta trace? Should I change the model?