-
-
Notifications
You must be signed in to change notification settings - Fork 230
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Misleading documentation for pymc.normal_like #175
Comments
Hi and thanks for the reply
My suggestion is to actually write the logarithm in the function, or state
explicitly that the function stated is the normal likelihood (and not the
normal log-likelihood)
Inga
…On 31 January 2018 at 01:52, Chris Fonnesbeck ***@***.***> wrote:
Closed #175 <pymc-devs/pymc#175>.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<pymc-devs/pymc#175 (comment)>, or mute
the thread
<https://github.com/notifications/unsubscribe-auth/ANoQKEtpqhjl1jPjBherOKXbcTb9xqxJks5tP7legaJpZM4RyAvH>
.
--
Inga Strümke
Skoglien 35
5056 Bergen
Norway
+47 95730153
+34 619371726
|
I see what you mean. The log-likelihood is just a computational convenience for doing more stable statistical computation, whereas users generally think of distributions in terms of its PDF/PMF, and not the log-transformed scale, so we present those in the documentation. Most users will not have to worry about the fact that the distributions are log-transformed, except when more advanced computations are required. That’s a good point, though, and we should probably state that the formula of PDF/PMF is displayed in the docs. |
Hi
Thank you for the reply, and for considering my concern.
Inga
…On 31 January 2018 at 17:28, Chris Fonnesbeck ***@***.***> wrote:
I see what you mean. The log-likelihood is just a computational
convenience for doing more stable statistical computation, whereas users
generally think of distributions in terms of its PDF/PMF, and not the
log-transformed scale, so we present those in the documentation. Most users
will not have to worry about the fact that the distributions are
log-transformed, except when more advanced computations are required.
That’s a good point, though, and we should probably state that the formula
of PDF/PMF is displayed in the docs.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<pymc-devs/pymc#175 (comment)>, or mute
the thread
<https://github.com/notifications/unsubscribe-auth/ANoQKC6TSzJKaVBTvbTKtA3SF_13IOU-ks5tQJSdgaJpZM4RyAvH>
.
--
Inga Strümke
Skoglien 35
5056 Bergen
Norway
+47 95730153
+34 619371726
|
Hi!
The documentation [1] for pymc.normal_like fails to mention that it actually returns the logarithm of the function, as the source code [2] reveals in the following lines
[1] https://pymc-devs.github.io/pymc/distributions.html#pymc.distributions.normal_like
[2] https://github.com/pymc-devs/pymc/blob/8733c6686787e0e98bd2445ea5408fe988adf0c9/pymc/flib.f
Thanks!
The text was updated successfully, but these errors were encountered: