I’m trying to infer a set of covariance matrixes for latent factors. My model looks something like the following for a single latent factor.

```
mu ~ Normal
sigma ~ Wishart
for (n=1...N)
x ~ MultivariateNormal(mu, tf.matrix_inverse(sigma))
...
```

I’m using `ed.KLqp`

and as long as I’m trying to infer `sigma`

, I observe that inference runs successfully for a some number of iterations, and then I hit:

```
InvalidArgumentError (see above for traceback): Cholesky decomposition was not successful. The input might not be valid.
```

I assume this means that the covariance matrix input to the multivariate normal isn’t positive definite (or otherwise acceptable?). I’ve tried both `MultivariateNormalTriL`

and `MultivariateNormalFullCovariance`

for `x`

and `WishartCholesky`

and `WishartFull`

for `sigma`

. I’ve also tried using `tf.cholesky`

and omitting `tf.matrix_inverse`

, just for kicks; the results are always the same. I’m just not sure how to go about debugging this; any help would be appreciated.