Thanks On Thu, May 26, 2016 at 12:57 PM, federico vaggi <[email protected]> wrote:
> Err, sorry - mu1, mu2, sigma1, sigma2, where mu1, sigma1 are the > mean/standard deviation of the first distribution, and mu2, sigma2 are the > mean and standard deviation of the second distribution. > > On Thu, 26 May 2016 at 09:26 federico vaggi <[email protected]> > wrote: > >> If you are talking about finding the values at which the probability >> density functions will have the same value, then you can just write the >> equations explicitly and solve in terms of theta1, sigma1 and theta2, >> sigma2? >> >> >> On Thu, 26 May 2016 at 09:23 Startup Hire <[email protected]> >> wrote: >> >>> Hi, >>> >>> (1) - Thanks. will do that >>> >>> (2) - I am fitting the distribution for 2 different set of values.. I >>> will find the distribution as mentioned by you in (1).. But, now having 2 >>> curves, how do i find the meetings point(s) ? >>> >>> Regards, >>> Sanant >>> >>> On Thu, May 26, 2016 at 12:16 PM, federico vaggi < >>> [email protected]> wrote: >>> >>>> 1) The normal distribution is parametrized by standard deviation and >>>> mean. Simply take the mean and standard deviation of the log of your >>>> values? >>>> >>>> 2) Which curves? You only mentioned a single log normal distribution. >>>> >>>> On Thu, 26 May 2016 at 08:42 Startup Hire <[email protected]> >>>> wrote: >>>> >>>>> Hi Michael, >>>>> >>>>> :) >>>>> >>>>> >>>>> (1) - I think you are right, how do I fit a normal distribution to >>>>> the log of values? >>>>> >>>>> (2) Intersection ---> Meeting point (s) . as in where the curves >>>>> cross each other (it can be in multiple places too!) >>>>> >>>>> >>>>> Regards, >>>>> Sanant >>>>> >>>>> On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg < >>>>> [email protected]> wrote: >>>>> >>>>>> Hi Sanant, >>>>>> >>>>>> On Thursday, May 26, 2016, Startup Hire <[email protected]> >>>>>> wrote: >>>>>> >>>>>>> Hi all, >>>>>>> >>>>>>> Hope you are doing good. >>>>>>> >>>>>> >>>>>> I would like to think so, but you never know where ML will lead us ... >>>>>> >>>>>> >>>>>>> >>>>>>> I am working on a project where I need to do the following things: >>>>>>> >>>>>>> 1. I need to fit a lognormal distribution to a set of values [I know >>>>>>> its lognormal by a simple XY scatter plot in excel] >>>>>>> >>>>>> >>>>>> if your distribution is lognormal, why don't you try fitting a >>>>>> gaussian to the log of the values? is this too unstable? >>>>>> >>>>>> >>>>>>> >>>>>>> 2. I need to find the intersection of the lognormal distribution so >>>>>>> that I can decide cut-off values based on that. >>>>>>> >>>>>> >>>>>> what exactly do you mean by intersection? >>>>>> >>>>>> >>>>>>> >>>>>>> >>>>>>> Can you guide me on (1) and (2) can be achieved in python? >>>>>>> >>>>>>> Regards, >>>>>>> Sanant >>>>>>> >>>>>> >>>>>> >>>>>> Michael >>>>>> >>>>>> _______________________________________________ >>>>>> scikit-learn mailing list >>>>>> [email protected] >>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>> >>>>>> >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> [email protected] >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> [email protected] >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> >>>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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