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stan reviews and mentions
What do actual ML engineers think of ChatGPT?
2 projects | /r/learnmachinelearning | 11 May 2023
I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but learning from their design and emulating what makes them successful.
ChatGPT4 writes Stan code so I don’t have to
2 projects | news.ycombinator.com | 23 Apr 2023
Automatic differentiation in C
2 projects | /r/computerscience | 4 Mar 2023
[D] Programming language for developing computational statistics algorithms
3 projects | /r/statistics | 30 Jan 2023
I'd say take a look at Stan (https://mc-stan.org/)3 projects | /r/statistics | 30 Jan 2023
Well it sounds a lot like you are listening to developers talk about coding languages they like for high performance compute. This is not what you want to be spending all your time doing afaik. The more appropriate languages to get into would be the classic Python and R. Julia if you dont give a shit about productionizing your code and https://mc-stan.org/ Stan if you are really locked into bayesian inference and wanted to pick julia anyway.
Is python necessary to learn machine learning?
2 projects | /r/learnmachinelearning | 20 Oct 2022
Even if RStudio & the Tidyverse have mostly been promoting a functional programming style in R, it has full support for OOP (see R6 or R7 for more modern implementations of it). Let's not even mention the excellent Stan ecosystem for Probabilistic programming / Bayesian modeling, or Bioconductor, the biggest repository of bioinformatics packages & tools of any language.
[Q] Updated book or review paper on MCMC methods
2 projects | /r/statistics | 13 Sep 2022
Stan in Nim?
5 projects | /r/nim | 25 May 2022
Step-by-step example of Bayesian t-test?
4 projects | /r/AskStatistics | 3 Apr 2022
Okay so first off, I recommend that you read [this](https://link.springer.com/article/10.3758/s13423-016-1221-4) article about "The Bayesian New Statistics", which highlights estimation rather than hypothesis testing from a Bayesian perspective (see Fig. 1, second row, second column). Instead of a t-test, then, we can *estimate the difference* between two groups/variables. If you want to go deeper than JASP etc, I recommend that you use [brms](https://paul-buerkner.github.io/brms/), or, if you want to go even deeper, [Stan](https://mc-stan.org/) (brms is a front-end to Stan).4 projects | /r/AskStatistics | 3 Apr 2022
The ` sigma ~ x` part specifies that sigma should be estimated separately for each group. Note that I'm also using scaled data, since then I can go by the Stan team's [prior choice recommendations](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations). We also specified ` family = gaussian` , which is telling the model to treat ` y` , the difference between the two variables, as normally distributed. In other words, this is the likelihood! There are [lots](https://cran.r-project.org/web/packages/brms/vignettes/brms_families.html) of "families" in brms. In particular, if you use a Student's t distribution instead, your model will be more robust against outliers!
A note from our sponsor - #<SponsorshipServiceOld:0x00007f09209ee140>
www.saashub.com | 8 Jun 2023
stan-dev/stan is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of stan is C++.