# Sampling ordered normal distributions with array-valued bounds

**URL:** <https://discourse.pymc.io/t/sampling-ordered-normal-distributions-with-array-valued-bounds/6392>\
**Category:** Questions\
**Created:** [December 7, 2020, 3:13pm UTC](https://discourse.pymc.io/t/sampling-ordered-normal-distributions-with-array-valued-bounds/6392 "2020-12-07T15:13:26Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![LegrandNico](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/legrandnico/32/5325_2.png) [@LegrandNico](https://discourse.pymc.io/u/LegrandNico)\
**Post date:** [December 7, 2020, 3:13pm UTC](https://discourse.pymc.io/t/sampling-ordered-normal-distributions-with-array-valued-bounds/6392/1 "2020-12-07T15:13:27Z")

</div>

Hi,  
I am trying to reproduce the following JAGS code in PyMC3, which sample from normal distributions given bounds defined in the matrice `beta[s,i]` and sort the distributions along the axis _j_.

```auto
for (s in 1:20){
  for (i in 1:4){
      for (j in 1:10){
        alpha_raw[s,j,i] ~ dnorm(0, 0.1) T(,beta[s,i])
        alpha[s,1:10,i] <- sort(alpha_raw[s,1:10,i])
}

```

I tried to use chained distribution transforms but did not managed to make it work correctly. Actually, even simpler one-dimensional models like the one bellow returned a `Bad initial energy` error.

```auto
import numpy as np
import pymc3 as pm
import pymc3.distributions.transforms as tr

beta = np.random.normal(0, 1, 10)
with pm.Model() as model:
  alpha = pm.Normal('alpha', 
                    mu=0, 
                    tau=0.01, 
                    shape=(10),
                    transform=tr.Chain([tr.LowerBound(beta), tr.Ordered()]),
                    testval=np.sort(np.random.normal(0, 1, 10) + 10))
  trace = pm.sample()

```

Here, I have two questions:

- What is incorrect in the code snippet above?
- Is it possible to generalize this code with more than 1 dimension? Especially, is it possible for the `Ordered()` transform to sort along one dimension only?

Thank you for your help.