like below fixes the issue When you create a your normal stochastastic with pymc.Normal('w0', 0, 0.000001), PyMC2 initializes the value with a random draw from the prior distribution. Since your prior is so diffuse, this can be a value which is so unlikely that the posterior is effectively zero. To fix, just request a reasonable initial value for your Normal: code :
w0 = pymc.Normal('w0', 0, 0.000001, value=0)
w1 = pymc.Normal('w1', 0, 0.000001, value=0)
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Why is the standard error different in these two fitting methods (R Logistic Regression and Beta Regression) for a commo
By : Random Guy
Date : March 29 2020, 07:55 AM
I wish this help you The standard errors are different because the variance assumptions in the two models are different. Logistic regression assumes the response has a binomial distribution, while beta regression assumes it has a beta distribution.

Logistic Regression in PyMC
By : user2210621
Date : March 29 2020, 07:55 AM
it fixes the issue You are definitely on the right track. I'm not solving your homework problem am I? It is now a preferred idiom to import pymc as pm instead of mc, so to finish this up with an observed decorator, just use: code :
import pymc as pm
@pm.observed
def y(logit_p=logit_p, value=df.GotSick):
return pm.bernoulli_like(df.GotSick, pm.invlogit(logit_p))

Fitting a Binomial distribution with pymc raises ZeroProbability error for certain FillValues
By : tsssys
Date : March 29 2020, 07:55 AM

Logistic regression without an intercept gives fitting warning message
By : calledT
Date : March 29 2020, 07:55 AM
Does that help I will try to provide an answer to the question. What does the warning mean? The warning is given when numerical precision might be in question for certain observations. More precisely, it is given in the case where the fitted model, returns probability of 1  epsilon or equivalently 0 + epsilon. As standard this bound is 110^8 and 10^8 respectively (as given by glm.control) for the standard glm.fit function.

Scikitlearn's logistic regression is performing poorer than selfwritten logistic regression in Python
By : randallcp
Date : March 29 2020, 07:55 AM

