On 2 February 2013 18:26, Wolfgang Keller <felip...@gmx.net> wrote: >> I am looking for a Python implementation of Maximum Likelihood >> Estimation. If any one can kindly suggest. With a google search it >> seems scipy,numpy,statsmodels have modules, but as I am not finding >> proper example workouts I am failing to use them. > > For statistics I would suggest using R (http://www.r-project.org/) > through RPy (http://rpy.sourceforge.net/). > > Both have dedicated mailinglists as well as extensive documentation.
I agree with Wolfgang that R is likely to be able to do what you want and that you may have better luck asking this kind of question on their mailing lists (or on the scipy mailing list). In any case, though, you will need to be more specific about what you mean. Maximum Likelihood Estimation (MLE) is a sufficiently vague topic that there cannot really be an "implementation" of it. What kind of model/data are you working with? Or are you working with pure probability distributions? What kind of parameters are you trying to find? Are the parameters you are trying to choose discrete or continuous? Are you trying to find one parameter or several simultaneously? Are you able to find an analytic solution that transforms your MLE problem into a specific kind of mathematical problem, such as solving a system of linear equations? Assuming that you are able to compute directly the likelihood (or log-likelihood) of whatever it is you are interested in, then your MLE problem is simply an optimisation problem, so you may have better luck searching for implementations of optimisation (you will still need to answer the questions above to be able choose an optimisation method). Oscar -- http://mail.python.org/mailman/listinfo/python-list
