#mcmc_stan — Public Fediverse posts

A #mcmc_stan #rstats question:

Is it possible to retrieve the seed used for a model fitted with cmdstanr?

In rstan there is get_seed, but can't find similar thing for cmdstanr

Thanks! @mcmc_stan

A #mcmc_stan #rstats question:

Is it possible to retrieve the seed used for a model fitted with cmdstanr?

In rstan there is get_seed, but can't find similar thing for cmdstanr

Thanks! @mcmc_stan

A #mcmc_stan #rstats question:

Is it possible to retrieve the seed used for a model fitted with cmdstanr?

In rstan there is get_seed, but can't find similar thing for cmdstanr

Thanks! @mcmc_stan

A #mcmc_stan #rstats question:

Is it possible to retrieve the seed used for a model fitted with cmdstanr?

In rstan there is get_seed, but can't find similar thing for cmdstanr

Thanks! @mcmc_stan

A #mcmc_stan #rstats question:

Is it possible to retrieve the seed used for a model fitted with cmdstanr?

In rstan there is get_seed, but can't find similar thing for cmdstanr

Thanks! @mcmc_stan

blavaan 0.5-1 is now on CRAN, including initial functionality for two-level structural equation models. Estimation happens via #mcmc_stan

If you don't know these models, they are multivariate Gaussian models with three levels (e.g., multiple response variables within people within schools).

Some further info is here:
https://ecmerkle.github.io/blavaan/articles/multilevel.html

blavaan 0.5-1 is now on CRAN, including initial functionality for two-level structural equation models. Estimation happens via #mcmc_stan

If you don't know these models, they are multivariate Gaussian models with three levels (e.g., multiple response variables within people within schools).

Some further info is here:
https://ecmerkle.github.io/blavaan/articles/multilevel.html

blavaan 0.5-1 is now on CRAN, including initial functionality for two-level structural equation models. Estimation happens via #mcmc_stan

If you don't know these models, they are multivariate Gaussian models with three levels (e.g., multiple response variables within people within schools).

Some further info is here:
https://ecmerkle.github.io/blavaan/articles/multilevel.html

blavaan 0.5-1 is now on CRAN, including initial functionality for two-level structural equation models. Estimation happens via #mcmc_stan

If you don't know these models, they are multivariate Gaussian models with three levels (e.g., multiple response variables within people within schools).

Some further info is here:
https://ecmerkle.github.io/blavaan/articles/multilevel.html

blavaan 0.5-1 is now on CRAN, including initial functionality for two-level structural equation models. Estimation happens via #mcmc_stan

If you don't know these models, they are multivariate Gaussian models with three levels (e.g., multiple response variables within people within schools).

Some further info is here:
https://ecmerkle.github.io/blavaan/articles/multilevel.html

New version of cmdstanr exposes Stan functions and allows you to directly unconstrain parameters (super useful for seeing parameters as the sampler sees them)! A huge win!

https://mc-stan.org/cmdstanr/news/index.html#cmdstanr-060

#Stan #cmdstanr #mcmc_stan #Bayes #rstats

New version of cmdstanr exposes Stan functions and allows you to directly unconstrain parameters (super useful for seeing parameters as the sampler sees them)! A huge win!

https://mc-stan.org/cmdstanr/news/index.html#cmdstanr-060

#Stan #cmdstanr #mcmc_stan #Bayes #rstats

New version of cmdstanr exposes Stan functions and allows you to directly unconstrain parameters (super useful for seeing parameters as the sampler sees them)! A huge win!

https://mc-stan.org/cmdstanr/news/index.html#cmdstanr-060

#Stan #cmdstanr #mcmc_stan #Bayes #rstats

New version of cmdstanr exposes Stan functions and allows you to directly unconstrain parameters (super useful for seeing parameters as the sampler sees them)! A huge win!

https://mc-stan.org/cmdstanr/news/index.html#cmdstanr-060

#Stan #cmdstanr #mcmc_stan #Bayes #rstats

New version of cmdstanr exposes Stan functions and allows you to directly unconstrain parameters (super useful for seeing parameters as the sampler sees them)! A huge win!

https://mc-stan.org/cmdstanr/news/index.html#cmdstanr-060

#Stan #cmdstanr #mcmc_stan #Bayes #rstats

Getting started here. Expect me to talk about #stats, #bayes, #brms, #mcmc_stan, #probprag, #emacs, #cogsci, #cogmod, #pragmatics and more.

Getting started here. Expect me to talk about #stats, #bayes, #brms, #mcmc_stan, #probprag, #emacs, #cogsci, #cogmod, #pragmatics and more.

Getting started here. Expect me to talk about #stats, #bayes, #brms, #mcmc_stan, #probprag, #emacs, #cogsci, #cogmod, #pragmatics and more.

Getting started here. Expect me to talk about #stats, #bayes, #brms, #mcmc_stan, #probprag, #emacs, #cogsci, #cogmod, #pragmatics and more.

Getting started here. Expect me to talk about #stats, #bayes, #brms, #mcmc_stan, #probprag, #emacs, #cogsci, #cogmod, #pragmatics and more.

Things are coming together for #ArviZ's InferenceData (https://github.com/arviz-devs/InferenceObjects.jl) to be a supported output type for #Turing and #JuliaLang's :julia: #Stan interface, similarly to how it is for #PyMC.

For details, see https://github.com/TuringLang/MCMCChains.jl/issues/381 and https://github.com/StanJulia/StanSample.jl/issues/60

#statistics #mcmc_stan #bayesian

Things are coming together for #ArviZ's InferenceData (https://github.com/arviz-devs/InferenceObjects.jl) to be a supported output type for #Turing and #JuliaLang's :julia: #Stan interface, similarly to how it is for #PyMC.

For details, see https://github.com/TuringLang/MCMCChains.jl/issues/381 and https://github.com/StanJulia/StanSample.jl/issues/60

#statistics #mcmc_stan #bayesian

Things are coming together for #ArviZ's InferenceData (https://github.com/arviz-devs/InferenceObjects.jl) to be a supported output type for #Turing and #JuliaLang's :julia: #Stan interface, similarly to how it is for #PyMC.

For details, see https://github.com/TuringLang/MCMCChains.jl/issues/381 and https://github.com/StanJulia/StanSample.jl/issues/60

#statistics #mcmc_stan #bayesian

A free online conference on Bayesian analysis of spatiotemporal data with the #Stan language tomorrow.

StanConnect 2022: "Stan Through Space and Time"

Date: October 31st, 8am-12:30pm EDT

There's an incredible lineup -- check out the speakers, abstracts and register now: https://eventbrite.com/e/stanconnect-2022-stan-through-space-and-time-tickets-440757677077

Hosted by Jamie Hogg and the Stan team.

#statistics #bayesian #conference #mcmc_stan

A free online conference on Bayesian analysis of spatiotemporal data with the #Stan language tomorrow.

StanConnect 2022: "Stan Through Space and Time"

Date: October 31st, 8am-12:30pm EDT

There's an incredible lineup -- check out the speakers, abstracts and register now: https://eventbrite.com/e/stanconnect-2022-stan-through-space-and-time-tickets-440757677077

Hosted by Jamie Hogg and the Stan team.

#statistics #bayesian #conference #mcmc_stan

A free online conference on Bayesian analysis of spatiotemporal data with the #Stan language tomorrow.

StanConnect 2022: "Stan Through Space and Time"

Date: October 31st, 8am-12:30pm EDT

There's an incredible lineup -- check out the speakers, abstracts and register now: https://eventbrite.com/e/stanconnect-2022-stan-through-space-and-time-tickets-440757677077

Hosted by Jamie Hogg and the Stan team.

#statistics #bayesian #conference #mcmc_stan