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Groups > comp.lang.python > #38045 > unrolled thread
| Started by | subhabangalore@gmail.com |
|---|---|
| First post | 2013-02-01 09:17 -0800 |
| Last post | 2013-02-02 21:41 +0000 |
| Articles | 11 — 8 participants |
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Maximum Likelihood Estimation subhabangalore@gmail.com - 2013-02-01 09:17 -0800
Re: Maximum Likelihood Estimation 88888 Dihedral <dihedral88888@googlemail.com> - 2013-02-01 09:37 -0800
Re: Maximum Likelihood Estimation subhabangalore@gmail.com - 2013-02-01 10:47 -0800
Re: Maximum Likelihood Estimation Michael Torrie <torriem@gmail.com> - 2013-02-01 11:59 -0700
Re: Maximum Likelihood Estimation Jerry Hill <malaclypse2@gmail.com> - 2013-02-01 14:14 -0500
RE: Maximum Likelihood Estimation tkhan10 <tkhan10@masonlive.gmu.edu> - 2013-02-01 19:01 +0000
RE: Maximum Likelihood Estimation Steven D'Aprano <steve+comp.lang.python@pearwood.info> - 2013-02-02 10:12 +1100
Re: Maximum Likelihood Estimation 88888 Dihedral <dihedral88888@googlemail.com> - 2013-02-01 11:55 -0800
Re: Maximum Likelihood Estimation subhabangalore@gmail.com - 2013-02-01 20:53 -0800
Re: Maximum Likelihood Estimation Wolfgang Keller <feliphil@gmx.net> - 2013-02-02 19:26 +0100
Re: Maximum Likelihood Estimation Oscar Benjamin <oscar.j.benjamin@gmail.com> - 2013-02-02 21:41 +0000
| From | subhabangalore@gmail.com |
|---|---|
| Date | 2013-02-01 09:17 -0800 |
| Subject | Maximum Likelihood Estimation |
| Message-ID | <e7aa8874-3692-4e32-ba52-5f4719761c24@googlegroups.com> |
Dear Group, 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. I am using Python 2.7 on Windows 7. Thanking You in Advance, Regards, Subhabrata
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| From | 88888 Dihedral <dihedral88888@googlemail.com> |
|---|---|
| Date | 2013-02-01 09:37 -0800 |
| Message-ID | <68027a3f-5320-44b7-a985-1d213062e2c0@googlegroups.com> |
| In reply to | #38045 |
subhaba...@gmail.com於 2013年2月2日星期六UTC+8上午1時17分04秒寫道: > Dear Group, > > > > 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. > > > > I am using Python 2.7 on Windows 7. > > > > Thanking You in Advance, > > > > Regards, > > Subhabrata I suggest you can google "python and symbolic computation" to get some package for your need first. Because it seems that you have to work out some math formula and verify some random process first of your data sources with noises .
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| From | subhabangalore@gmail.com |
|---|---|
| Date | 2013-02-01 10:47 -0800 |
| Message-ID | <e59815da-c08a-4cca-af88-7d148a7bceaa@googlegroups.com> |
| In reply to | #38046 |
On Friday, February 1, 2013 11:07:48 PM UTC+5:30, 88888 Dihedral wrote: > subhaba...@gmail.com於 2013年2月2日星期六UTC+8上午1時17分04秒寫道: > > > Dear Group, > > > > > > > > > > > > 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. > > > > > > > > > > > > I am using Python 2.7 on Windows 7. > > > > > > > > > > > > Thanking You in Advance, > > > > > > > > > > > > Regards, > > > > > > Subhabrata > > > > I suggest you can google "python and symbolic > > computation" to get some package for your need first. > > > > Because it seems that you have to work out some > > math formula and verify some random process first > > of your data sources with noises . Dear Group, Thanks. I googled and found a new package named Sympy and could generate MLE graphs. Regards,Subhabrata.
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| From | Michael Torrie <torriem@gmail.com> |
|---|---|
| Date | 2013-02-01 11:59 -0700 |
| Message-ID | <mailman.1282.1359745161.2939.python-list@python.org> |
| In reply to | #38047 |
On 02/01/2013 11:47 AM, subhabangalore@gmail.com wrote: > On Friday, February 1, 2013 11:07:48 PM UTC+5:30, 88888 Dihedral > wrote: >> subhaba...@gmail.com於 2013年2月2日星期六UTC+8上午1時17分04秒寫道: >>> 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. >> >> I suggest you can google "python and symbolic computation" to get >> some package for your need first. Because it seems that you have to >> work out some math formula and verify some random process first of >> your data sources with noises . > > Dear Group, Thanks. I googled and found a new package named Sympy and > could generate MLE graphs. Regards,Subhabrata. My hat's off to you for actually interpreting the 88888 dihedral robot's randomly-created response in a way that actually helped you find the solution to your problem! I think that's a python first around here. Most people on this list consider 88888 dihedral to be a badly programmed bot.
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| From | Jerry Hill <malaclypse2@gmail.com> |
|---|---|
| Date | 2013-02-01 14:14 -0500 |
| Message-ID | <mailman.1283.1359746084.2939.python-list@python.org> |
| In reply to | #38047 |
On Fri, Feb 1, 2013 at 1:59 PM, Michael Torrie <torriem@gmail.com> wrote: > Most people on this list consider 88888 dihedral to be a badly > programmed bot. For what it's worth, I think it's a very cleverly programmed bot. It usually makes just enough sense for me to wonder if there really is a human being behind the keyboard. -- Jerry
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| From | tkhan10 <tkhan10@masonlive.gmu.edu> |
|---|---|
| Date | 2013-02-01 19:01 +0000 |
| Message-ID | <mailman.1284.1359747126.2939.python-list@python.org> |
| In reply to | #38047 |
Hi.. I know this is a very dumb q but actually I am new to python and to this list. I want to post a question about geographic masking but cannot find out how to post it. Would somebody please suggest me how to do that? Thank you Subrina ________________________________________ From: Python-list [python-list-bounces+tkhan10=gmu.edu@python.org] on behalf of subhabangalore@gmail.com [subhabangalore@gmail.com] Sent: Friday, February 01, 2013 1:47 PM To: python-list@python.org Subject: Re: Maximum Likelihood Estimation On Friday, February 1, 2013 11:07:48 PM UTC+5:30, 88888 Dihedral wrote: > subhaba...@gmail.com於 2013年2月2日星期六UTC+8上午1時17分04秒寫道: > > > Dear Group, > > > > > > > > > > > > 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. > > > > > > > > > > > > I am using Python 2.7 on Windows 7. > > > > > > > > > > > > Thanking You in Advance, > > > > > > > > > > > > Regards, > > > > > > Subhabrata > > > > I suggest you can google "python and symbolic > > computation" to get some package for your need first. > > > > Because it seems that you have to work out some > > math formula and verify some random process first > > of your data sources with noises . Dear Group, Thanks. I googled and found a new package named Sympy and could generate MLE graphs. Regards,Subhabrata. -- http://mail.python.org/mailman/listinfo/python-list
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| From | Steven D'Aprano <steve+comp.lang.python@pearwood.info> |
|---|---|
| Date | 2013-02-02 10:12 +1100 |
| Message-ID | <510c4bd9$0$29978$c3e8da3$5496439d@news.astraweb.com> |
| In reply to | #38050 |
tkhan10 wrote: > Hi.. > I know this is a very dumb q but actually I am new to python and to this > list. I want to post a question about geographic masking but cannot find > out how to post it. Would somebody please suggest me how to do that? Thank > you Subrina Are you using email or Usenet? In your email client: 1) Create a new email message; 2) Type the To address python-list@python.org 3) Type a meaningful subject line such as "Geographic Masking in Python" 4) Type your message. 5) Hit send. Or, if you are using Usenet, in your news client: 1) Create a new news post; 2) Type the To address comp.lang.python; 3) Type a meaningful subject line such as "Geographic Masking in Python" 4) Type your message. 5) Hit send. -- Steven
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| From | 88888 Dihedral <dihedral88888@googlemail.com> |
|---|---|
| Date | 2013-02-01 11:55 -0800 |
| Message-ID | <dede28eb-a1b5-45fb-ac30-42836b2b51e6@googlegroups.com> |
| In reply to | #38047 |
在 2013年2月2日星期六UTC+8上午2时47分22秒,subhaba...@gmail.com写道: > On Friday, February 1, 2013 11:07:48 PM UTC+5:30, 88888 Dihedral wrote: > > > subhaba...@gmail.com於 2013年2月2日星期六UTC+8上午1時17分04秒寫道: > > > > > > > Dear Group, > > > > > > > > > > > > > > > > > > > > > > > > > > > > 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. > > > > > > > > > > > > > > > > > > > > > > > > > > > > I am using Python 2.7 on Windows 7. > > > > > > > > > > > > > > > > > > > > > > > > > > > > Thanking You in Advance, > > > > > > > > > > > > > > > > > > > > > > > > > > > > Regards, > > > > > > > > > > > > > > Subhabrata > > > > > > > > > > > > I suggest you can google "python and symbolic > > > > > > computation" to get some package for your need first. > > > > > > > > > > > > Because it seems that you have to work out some > > > > > > math formula and verify some random process first > > > > > > of your data sources with noises . > > > > Dear Group, > > Thanks. I googled and found a new package named Sympy and could generate MLE graphs. Regards,Subhabrata. Well,just reveal more about your problems. But if you are concerned with some commercial problems, then it is not the novice apprentice level jokes. Then, maybe you can just give some outline of the problem.
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| From | subhabangalore@gmail.com |
|---|---|
| Date | 2013-02-01 20:53 -0800 |
| Message-ID | <b809d290-b5da-40e8-91c0-23bbd10e9efa@googlegroups.com> |
| In reply to | #38045 |
On Friday, February 1, 2013 10:47:04 PM UTC+5:30, subhaba...@gmail.com wrote: > Dear Group, > > > > 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. > > > > I am using Python 2.7 on Windows 7. > > > > Thanking You in Advance, > > > > Regards, > > Subhabrata Dear Sir, The room would take care of you. They still guide me and sometimes the way they rebuke you have to see. You are in a nice room, you'd learn soon. It was bot? If you are testing please let me know I'd like to be part of the testing, and if you suggest may volunteer to send queries. Regards, Subhabrata.
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| From | Wolfgang Keller <feliphil@gmx.net> |
|---|---|
| Date | 2013-02-02 19:26 +0100 |
| Message-ID | <20130202192609.0257968c157cdabdde805114@gmx.net> |
| In reply to | #38045 |
> 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. Sincerely, Wolfgang
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| From | Oscar Benjamin <oscar.j.benjamin@gmail.com> |
|---|---|
| Date | 2013-02-02 21:41 +0000 |
| Message-ID | <mailman.1302.1359841280.2939.python-list@python.org> |
| In reply to | #38085 |
On 2 February 2013 18:26, Wolfgang Keller <feliphil@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
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