Bayesian estimation with MCMC

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Bayesian estimation with MCMC

Bogaso
Hi,

In python there is a package called pymc3 for Bayesian parameter
estimation with MCMC.

I am curious if there is any equivalent package available for R.

Any pointer will be highly appreciated.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: Bayesian estimation with MCMC

Eric Berger
Hi Christofer,
Did you try web search? I entered 'R CRAN Bayesian parameter
estimation with MCMC'
and it came back with the following which seems relevant.

https://cran.r-project.org/web/packages/airGR/vignettes/V02.2_param_mcmc.html

There are other search results, such as:
https://cran.r-project.org/web/packages/deBInfer/index.html

You should note that using R does not exclude using pymc3 (or any
other Python package) from R.
The R package reticulate makes it easy to use python packages from an R session.
See https://cran.r-project.org/web/packages/reticulate/index.html

HTH,
Eric

On Sat, May 23, 2020 at 12:29 PM Christofer Bogaso
<[hidden email]> wrote:

>
> Hi,
>
> In python there is a package called pymc3 for Bayesian parameter
> estimation with MCMC.
>
> I am curious if there is any equivalent package available for R.
>
> Any pointer will be highly appreciated.
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Bayesian estimation with MCMC

Ivan Krylov
In reply to this post by Bogaso
On Sat, 23 May 2020 14:58:34 +0530
Christofer Bogaso <[hidden email]> wrote:

>Any pointer will be highly appreciated.

"Getting help with R" [1] describes several ways of looking for
R-related things. In particular, there is a Bayesian CRAN Task View
[2], which (as of now) has 52 occurrences of "MCMC".

--
Best regards,
Ivan

[1] https://www.r-project.org/help.html
[2] https://cran.r-project.org/view=Bayesian

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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Re: Bayesian estimation with MCMC

glsnow
In reply to this post by Bogaso
On CRAN there are Task Views which are moderated lists and summaries
of packages available for specific topics.  The first one
(alphabetically) is the Bayesian task view:
https://cran.r-project.org/web/views/Bayesian.html which lists many
packages for doing/learning Bayesian statistics using R.  There are
descriptions, categories, etc.  Looking through that list you will see
many packages that do McMC for Bayesian inference.

On Sat, May 23, 2020 at 3:29 AM Christofer Bogaso
<[hidden email]> wrote:

>
> Hi,
>
> In python there is a package called pymc3 for Bayesian parameter
> estimation with MCMC.
>
> I am curious if there is any equivalent package available for R.
>
> Any pointer will be highly appreciated.
>
> ______________________________________________
> [hidden email] mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.



--
Gregory (Greg) L. Snow Ph.D.
[hidden email]

______________________________________________
[hidden email] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.