<div dir="ltr">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.</div><br><div class="gmail_quote"><div dir="ltr">On Thu, 26 May 2016 at 09:26 federico vaggi <<a href="mailto:vaggi.federico@gmail.com">vaggi.federico@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">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?<div><br></div></div><br><div class="gmail_quote"><div dir="ltr">On Thu, 26 May 2016 at 09:23 Startup Hire <<a href="mailto:blrstartuphire@gmail.com" target="_blank">blrstartuphire@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi,<div><br></div><div>(1) - Thanks. will do that</div><div><br></div><div>(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) ? </div><div><br></div><div>Regards,</div><div>Sanant</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, May 26, 2016 at 12:16 PM, federico vaggi <span dir="ltr"><<a href="mailto:vaggi.federico@gmail.com" target="_blank">vaggi.federico@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">1) The normal distribution is parametrized by standard deviation and mean. Simply take the mean and standard deviation of the log of your values?<div><br></div><div>2) Which curves? You only mentioned a single log normal distribution.</div></div><div><div><br><div class="gmail_quote"><div dir="ltr">On Thu, 26 May 2016 at 08:42 Startup Hire <<a href="mailto:blrstartuphire@gmail.com" target="_blank">blrstartuphire@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Michael,<div><br></div><div>:)</div><div><br></div><div><br></div><div>(1) - I think you are right, how do I fit a normal distribution to the log of values?</div><div><br></div><div>(2) Intersection ---> Meeting point (s) . as in where the curves cross each other (it can be in multiple places too!) </div><div><br></div><div><br></div><div>Regards,</div><div>Sanant</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, May 26, 2016 at 11:52 AM, Michael Eickenberg <span dir="ltr"><<a href="mailto:michael.eickenberg@gmail.com" target="_blank">michael.eickenberg@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Sanant,<span><br><br>On Thursday, May 26, 2016, Startup Hire <<a href="mailto:blrstartuphire@gmail.com" target="_blank">blrstartuphire@gmail.com</a>> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi all,<div><br></div><div>Hope you are doing good.</div></div></blockquote><div><br></div></span>I would like to think so, but you never know where ML will lead us ...<span><span></span><br><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><br></div><div>I am working on a project where I need to do the following things:</div><div><br></div><div>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]</div></div></blockquote><div><br></div></span><div>if your distribution is lognormal, why don't you try fitting a gaussian to the log of the values? is this too unstable?</div><span><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><br></div><div>2. I need to find the intersection of the lognormal distribution so that I can decide cut-off values based on that.</div></div></blockquote><div><br></div></span><div>what exactly do you mean by intersection?</div><div><div><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div><br></div><div><br></div><div>Can you guide me on (1) and (2) can be achieved in python?</div><div><br></div><div>Regards,</div><div>Sanant</div></div></blockquote><div><br></div><div><br></div></div></div><span><font color="#888888"><div>Michael </div>
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