Stan Alternatives
Similar projects and alternatives to stan



Scout APM
Scout APM: A developer's best friend. Try free for 14days. Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.

fastbaps
A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data

Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

PyMC
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara




brms
brms R package for Bayesian generalized multivariate nonlinear multilevel models using Stan
Reviews and mentions

An Introduction to Probabilistic Programming
Probabilistic programming uses computer science techniques to do automated statistical modeling. For example, imagine I have a coin, and I want to discover if it is biased, i.e. if it lands on heads more often than tails. In a probabilistic programming framework, I can express my model as a simple Bernoulli model, `x ~ Bernoulli(p)`, and then automatically estimate the bias parameter `p` given some data (do "inference").
You can easily do this calculation by hand or in Python, but this does not generalize to more complex realworld scenarios. For complex probabilistic models, we must rely on numerical approximations. MCMC is just one algorithm for doing this approximate inference. Another popular technique is called variational inference [2].
Another commenter mentioned HMC [3], which is just a specific instance of MCMC.

Best computer language for stats
Not a language, but Stan is worth looking into if you are interested in Bayesian modeling.

What is Probabilistic Programming?
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are  Stan (https://mcstan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arvizdevs.github.io/arviz/), which can be used for all the abovementioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing

How can I factor is precision when comparing different multivariate regressions?
The only way I know how to do this sort of thing is by using an advanced regression system like Stan.

Is there a good forum to ask questions about RJAGS?
Note that Stan models generally use different priors than JAGS/BUGS, since conjugacy isn't nearly as important for efficiency. It's worth reading the developer's prior choice recommendations.

[OC] Euro 2020 (played in 2021) Group Stage Predictions Based of a Bayesian Linear Item Response Model
The model was built in Stan and was inspired by Andrew Gelman's World Cup model shown here. These plots show posterior probabilities that the team on the y axis will score more goals than the team on the x axis. There is some reduncancy of information here (because if I know P(England beats Scotland) then I know P(Scotland beats England) )

Weakly informative priors and scaling of categorical variables for Bayesian logistic regression
Once data is scaled in this way, [Gelman et al. (2008)] and Gelman again in [Stan Prior Choice Guidance](https://github.com/standev/stan/wiki/PriorChoiceRecommendations) recomend using a (scaled) Student's t distribution with $3

I have a small sample size time series with potentially lagged predictor values which are also time series. What could be potential methods to analyse these data?
Otherwise, you can just put a Gaussian prior centered on zero and you're good to go. It provides less information, but it's still better than the implicit uniform prior of Frequentist methods that assume minus 5 trillion is as likely as 2 for the growth rate of a chick. Prior Predictive Checks (aka Posterior Predictive Checks on priors only, before they are updated by the data) will allow you to visualize if the combination of priors you provided generates realistic data, so it's easy to adjust them. For more info, you can check this and this.

Paradigms for statistical inference
You can only get a posterior distribution by writing out a full model with priors and a likelihood. A Bayesian model consists of a likelihood and some number of layers of prior distributions. The product of those probability densities is proportional to the posterior probability density. And what you care about is the entire posterior distribution. Once you have that, all you have to do is choose how to summarize it. The difficulties in Bayesian inference come in the forms of laying out the model (often the likelihood is obvious while the priors are lessso), and getting samples from the posterior. Incalculable blood, sweat, and tears have gone into ways to get samples from this distribution. But we have things like stan for that these days, so Bayesian inference gets to proceed top down from a modelfirst perspective.

Uncertain Types part 1 (featuring Maria Gorinova)
Maria Gorinova’s Website Stan programming language Uncertain: A FirstOrder Type for Uncertain Data
 An Introduction to Hierarchical Modeling

[D] Baysaian Statistics: Making Use of a Prior
 Use the prior recommendations here: https://github.com/standev/stan/wiki/PriorChoiceRecommendations, along with how you modify them if you have knowledge

planning to invest my time on the turing.jl package and I do have a few questions
If your are only starting to familiarize yourself with Bayesian statistics, I would strongly recommend Stan (https://mcstan.org) over anything that Julia currently has to offer. It is under very active development, much more mature than Turing.jl, and its online documentation and community are very helpful.

Computational chemistry help, specifically to do with Bayesian chemistry analysis
I'm just trying Stan(https://mcstan.org/) on LCDAD , MS spectrum data for calculating confidence interval of the peak area.

[Research] How Bayesian Statistics convinced me to sleep more
You can read more about general guidelines on how to set priors here.
Stats
standev/stan is an open source project licensed under BSD 3clause "New" or "Revised" License which is an OSI approved license.
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