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The Value of Probabilistic Thinking: Spies, Crime, and Lightning Strikes
Probabilistic thinking is a useful tool to help us evaluate potential outcomes so we can make the best possible decision. Learn why it matters, and how to use this powerful mental model.
probability  think  bayes  4*
july 2018 by ianweatherhogg
The Mathematics of 2048: Optimal Play with Markov Decision Processes
Finding provably optimal strategies for 2048 using Markov Decision Processes
markov  decision  tree  probability
april 2018 by ianweatherhogg
Algorithmic Art - Tyler Hobbs - Working with Color in Generative Art
Techniques for working with color in generative artwork, by Tyler Hobbs
clojure  generator  art  probability
january 2018 by ianweatherhogg
Prophet - Time series prediction | Hardik Patel
Predicting daily (and intraday) volume is a classic time series problem in finance. We try to use the Prophet library for this task.
python  probability  statistics  face  book  stan  predictive
december 2017 by ianweatherhogg
Probabilistic programming from scratch
This article contains highlights from a series of three interactive video tutorials on probabilistic programming from scratch published on O’Reilly Safari (l...
stan  probability
july 2017 by ianweatherhogg
The Algorithms Behind Probabilistic Programming
We recently introduced our report on probabilistic programming. The accompanying prototype allows you to explore the past and future of the New York resident...
probability  programming  bayes  montecarlo
january 2017 by ianweatherhogg
What are the foundational structures of probabilities? How do we designa language making it easy to model probabilistic problems? Oftentimesthe modeling happens directly in terms of vectors and mat...
december 2016 by ianweatherhogg
Time Series Analysis (TSA) in Python - Linear Models to GARCH — BLACKARBS LLC
Post Outline
* Motivation
* The Basics
o Stationarity
o Serial Correlation (Autocorrelation)
o Why do we care about Serial Correlation?
* White Noise and Random Walks
* Linear Models
* Log-Linear Models
* Autoregressive Models - AR(p)
* Moving Average Models - MA(q)
* Autoregressive Moving Average Models - ARMA(p, q)
* Autoregressive Integrated Moving Average Models - ARIMA(p, d, q)
* Autoregressive Conditionally H...
python  time  series  statistics  probability  5*
december 2016 by ianweatherhogg
Bayesian Methods for Hackers
Bayesian Methods for Hackers : An intro to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view.
book  free  ipython  python  bayes  statistics  probability  5*
august 2016 by ianweatherhogg
Napkin Folding Blog – DataOrigami
Sister blog of Data Origami: Machine Learning, Data Science, Statistics and Python.
python  machine  learn  statistics  probability  ipython  5*
march 2015 by ianweatherhogg
Clustered Random Numbers
Using functional programming techniques and the standard functions from Underscore to generate random numbers that favor particular numbers. Useful for picking colors.
random  number  javascript  underscore  probability
october 2014 by ianweatherhogg
Property Testing in Ruby - jtobin.ca
Testing properties of Haskell functions with
QuickCheck is easy and pretty
enjoyable. It turns out Ruby has a QuickCheck-like property testing …
ruby  quick  check  dice  probability
september 2014 by ianweatherhogg
Stan modeling language and C++ library for Bayesian inference.
NUTS adaptive HMC (MCMC) sampling, automatic differentiation,
R, shell interfaces. Gelman.
probability  programming  language
may 2014 by ianweatherhogg
Distributions in Excel 2007
Working with probability distribution functions in Microsoft Excel.
excel  probability
may 2014 by ianweatherhogg
Brief introduction to Scala and Breeze for statistical computing | Darren Wilkinson's research blog
Introduction In the previous post I outlined why I think Scala is a good language for statistical computing and data science. In this post I want to give a quick taste of Scala and the Breeze numerical library to whet the appetite of the uninitiated. This post certainly won't provide enough material to get started…
scala  math  repl  session  probability  statistics  matrix  5*
january 2014 by ianweatherhogg
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