#### Topics covered

artificial-intelligence
artificial-lift
batch-automation
business-case
cloud
computational-physics
computer-science
conference
courses
data-driven-vs-physics
data-engineering
data-science
data-scientists
data-structures
datasets
deep-learning
engineering-library
fluid-properties
gas-lift
geology
geophysics
geoscience
latex
linux
literate-programming
logs
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machine-learning
modeling
open-source-software
paper-research
petroleum-engineering
petrophysics
production-technology
prosper
pvt
python
r
r-package
reproducibility
reservoir-engineering
reservoir-simulation
seismic
shiny
spe
statistics
text-mining
tikz
to-do
transcript
virtualization
visualization-of-data
vlp
volve
webapp
well
well-data
well-logs
well-modeling

# Subject ▸ statistics

## Building the book "Statistical Rethinking" by Solomon Kurtz in Docker

kurtz-rethinking bookdown This book was written by Solomon Kurtz which code lives here. It is a beautiful book on Bayesian regression in R using the brms package. Based on the book “Statistical Rethinking” by Richard McElreath, a Bayesian Course with examples in R and Stan. Bookdown details Book version 1.0.1. See index.Rmd. R-3.6.3 RStudio 1.2.5042 Most packages MRAN dated on 2019-06-12. Other packages dated at later dates for smnoother book building.## Calculate economic risk with regression using Python by Matteo Niccoli

Another reproducible example of regression using Python to calculate economic risk. By Matteo Niccoli (2017).

Keywords:

## References

## Multiple Regression in Engineering Applications by Marco Rizk

Great article. And also an eye opener, specially, for those interested in correlations. I hope you publish your scripts, data and manuscript, soon for reproducibility purposes, and the benefit of the petroleum engineering community.

**Keywords**: linear regression, engineering, Alternating Conditional Expectations, algorithms, transformations

## References

## Starting Bayesian with Stan

Starting Bayesian #Machinelearning with #stan and #rstats. Surprised by the fact that model recipes written in R and Stan are compiled to C++ using whatever number of cores your machine has.

- Correction: Bayesian machine learning -> Probabilistic Machine Learning