stat_rethinking_2020
stan
stat_rethinking_2020  stan  

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stat_rethinking_2020

[Q] Book on Bayesian statistics?
Bayesian rethinking is quite a good book and has been translated to Python.
 [E] Statistical Rethinking 2022 by Richard McElreath

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?
If you want a full course on Bayesian Multilevel models, there's the excellent "statistical rethinking": lectures/content here and code here

How to incorporate Bayes Inference into inplay betting model using R?
If you're unfamiliar with Bayesian analysis, I recommend reading Richard McElreath's Statistical Rethinking. It has associated R exercises and a lectures (found here)
 Any good video series for learning Bayesian stats?

Quantitative Methods Course
A wonderful introductory course from a Bayesian point of view: https://github.com/rmcelreath/stat_rethinking_2020

Any resource suggestion for 6420 Bayesian Statistics?
https://github.com/rmcelreath/stat_rethinking_2020 includes slides and videos.
stan
 Stan: Statistical modeling and highperformance statistical computation

Elevate Your Python Skills: Machine Learning Packages That Transformed My Journey as ML Engineer
Alternatives: stan and edward
 How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?

Rstan Package in ATPA
remove.packages(c("StanHeaders", "rstan")) install.packages("rstan", repos = c("https://mcstan.org/rpackages/", getOption("repos")))

[Q] Is there a method for adding random effects to an interval censored time to event model?
My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions.

HELP Conjugate Priors in Bayesian Regression in SPSS
Here is a good breakdown of recommendations from Andrew Gelman.

Demand Planning
For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes.

What do actual ML engineers think of ChatGPT?
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

How to get started learning modern AI?
oh its certainly used in practice. you should look into frameworks like Stan[1] and pyro[2]. i think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing
[1] https://mcstan.org/
What are some alternatives?
stat_rethinking_2022  Statistical Rethinking course winter 2022
PyMC  Bayesian Modeling and Probabilistic Programming in Python
brms  brms R package for Bayesian generalized multivariate nonlinear multilevel models using Stan
jax  Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
rstan  RStan, the R interface to Stan
EloMMR  Skill estimation systems for multiplayer competitions
probability  Probabilistic reasoning and statistical analysis in TensorFlow
rnim  A bridge between R and Nim
pyro  Deep universal probabilistic programming with Python and PyTorch
MultiBUGS  Multicore BUGS for fast Bayesian inference of large hierarchical models
fastbaps  A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data