# Numerical issues with Multinomial

**URL:** <https://discourse.edwardlib.org/t/numerical-issues-with-multinomial/540>\
**Category:** General\
**Created:** [December 13, 2017, 9:41pm UTC](https://discourse.edwardlib.org/t/numerical-issues-with-multinomial/540 "2017-12-13T21:41:52Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![mortonjt](https://avatars.discourse-cdn.com/v4/letter/m/3bc359/32.png) [@mortonjt](https://discourse.edwardlib.org/u/mortonjt)\
**Post date:** [January 3, 2018, 5:47pm UTC](https://discourse.edwardlib.org/t/numerical-issues-with-multinomial/540/2 "2018-01-03T17:47:09Z")

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Just as a heads up - I got something working using the MAP estimator. It seems that once I reduce the learning rate down sufficiently, I can get something that looks sane.

Here’s the code I’m using to do this.

```python
## Model
# N = number of samples
# p = number of covariates in regression model
# D = number of features within each sample
# psi = predefined orthonormal basis to ensure identifiability when performing the softmax transform
G = tf.placeholder(tf.float32, [N, p])
B = Normal(loc=tf.zeros([p, D-1]), 
           scale=tf.ones([p, D-1]))
v = tf.matmul(G, B) 
eta = tf.nn.log_softmax(tf.matmul(v, psi))
Y = Multinomial(total_count=n, logits=eta, 
                value=tf.zeros([N, D], dtype=tf.float32))

## Inference
qB = PointMass(params=tf.Variable(tf.zeros([p, D-1])))
inference = ed.MAP(
    {B: qB},     
    data={G: G_data, Y: y_data}
)
optimizer = tf.train.AdamOptimizer(5e-4, 
                                   beta1=0.9,
                                   beta2=0.999)
inference.run(n_iter=5000, optimizer=optimizer)

```

Still having a bit of trouble getting the other inference techniques to work. Could be due to difficulties handling the Multinomial. But I’m not entirely sure.

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