# How to access point estimates of parameters

**URL:** <https://discourse.edwardlib.org/t/how-to-access-point-estimates-of-parameters/53>\
**Category:** General\
**Created:** [March 22, 2017, 1:45am UTC](https://discourse.edwardlib.org/t/how-to-access-point-estimates-of-parameters/53 "2017-03-22T01:45:57Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![willtownes](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.edwardlib.org/willtownes/32/21_2.png) [@willtownes](https://discourse.edwardlib.org/u/willtownes)\
**Post date:** [March 23, 2017, 7:32pm UTC](https://discourse.edwardlib.org/t/how-to-access-point-estimates-of-parameters/53/3 "2017-03-23T19:32:12Z")

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Thanks for your reply!

My underlying question is, can I use this tool to do pure maximum likelihood?

I can get it to work sometimes but not always. I came up with a simpler example of just finding the mean and sd of a univariate gaussian. Here is the code:

```auto
mu_true = 5
sd_true = 2
N = 100
xtrain = np.random.normal(mu_true,sd_true,size=N)
#edward model
s = tf.Variable(1.0)
m = tf.Variable(0.0)
x = Normal(mu=m*tf.ones(N),sigma=s*tf.ones(N))
mle = ed.Inference({},{x:xtrain})
mle.run()
sess = ed.get_session()
sess.run(m)

```

expected result: $m\approx 5$. Observed result: $m=0.0$.

If I put a prior on m and do MAP I can get the result out from the qm approximating PointMass distribution with no issue. Also interestingly, if I put a prior on m, I can get the correct point estimate for “s” using sess.run(s) as expected, but if I don’t put a prior on m, I get the initialized (incorrect) point estimate.

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