PyMC3: how to efficiently regress on many variables?
$begingroup$
I am sorry ahead of time if this seems like a basic question, but I had difficulty finding resources online addressing this. In PyMC3, when building a basic model of a few variables, it is easy to define each of them, like alpha=pm.Normal('alpha',mu=0,st=1), and manually add them all with each other. However, what are the standard approaches when one is dealing with dozens/hundreds of variables, each needing a prior?
I see that the shape argument is helpful in defining many priors at once, but after that how do you then add them all together in the mean equation (if your model specifies a Normal distribution), or what if different groups of variables need different priors?
I would heavily appreciate any advice on this matter, as I'm switching over to PyMC3 from regular machine learning and regression.
python bigdata data bayesian
asked 25 mins ago
Coolio2654Coolio2654
1183
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add a comment |
$begingroup$
I am sorry ahead of time if this seems like a basic question, but I had difficulty finding resources online addressing this. In PyMC3, when building a basic model of a few variables, it is easy to define each of them, like alpha=pm.Normal('alpha',mu=0,st=1), and manually add them all with each other. However, what are the standard approaches when one is dealing with dozens/hundreds of variables, each needing a prior?
I see that the shape argument is helpful in defining many priors at once, but after that how do you then add them all together in the mean equation (if your model specifies a Normal distribution), or what if different groups of variables need different priors?
I would heavily appreciate any advice on this matter, as I'm switching over to PyMC3 from regular machine learning and regression.
python bigdata data bayesian
asked 25 mins ago
Coolio2654Coolio2654
1183
$endgroup$
add a comment |
$begingroup$
I am sorry ahead of time if this seems like a basic question, but I had difficulty finding resources online addressing this. In PyMC3, when building a basic model of a few variables, it is easy to define each of them, like alpha=pm.Normal('alpha',mu=0,st=1), and manually add them all with each other. However, what are the standard approaches when one is dealing with dozens/hundreds of variables, each needing a prior?
I see that the shape argument is helpful in defining many priors at once, but after that how do you then add them all together in the mean equation (if your model specifies a Normal distribution), or what if different groups of variables need different priors?
I would heavily appreciate any advice on this matter, as I'm switching over to PyMC3 from regular machine learning and regression.
python bigdata data bayesian
asked 25 mins ago
Coolio2654Coolio2654
1183
$endgroup$
I am sorry ahead of time if this seems like a basic question, but I had difficulty finding resources online addressing this. In PyMC3, when building a basic model of a few variables, it is easy to define each of them, like alpha=pm.Normal('alpha',mu=0,st=1), and manually add them all with each other. However, what are the standard approaches when one is dealing with dozens/hundreds of variables, each needing a prior?
I see that the shape argument is helpful in defining many priors at once, but after that how do you then add them all together in the mean equation (if your model specifies a Normal distribution), or what if different groups of variables need different priors?
I would heavily appreciate any advice on this matter, as I'm switching over to PyMC3 from regular machine learning and regression.
python bigdata data bayesian
python bigdata data bayesian
asked 25 mins ago
Coolio2654Coolio2654
1183
asked 25 mins ago
Coolio2654Coolio2654
1183
asked 25 mins ago
Coolio2654Coolio2654
1183
asked 25 mins ago
Coolio2654Coolio2654
1183
asked 25 mins ago
Coolio2654Coolio2654
1183
1183
add a comment |
add a comment |
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