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sdwfrost / ModelingWithJulia.ipynb
Last active August 17, 2017 11:48
Some examples of mathematical modeling and simple MCMC in Julia
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@sdwfrost
sdwfrost / ModelingWithR.ipynb
Created January 16, 2014 13:17
Some examples of mathematical modeling and simple MCMC in R, including some Rcpp examples
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@sdwfrost
sdwfrost / sir_example.html
Last active August 29, 2015 13:56
An example of an SIR epidemiological model in Agentscript
<html>
<head>
<title>SIR Model</title>
<script src="http://agentscript.org/lib/agentscript.js"></script>
<script src="http://agentscript.org/tools/coffee-script.js"></script>
<script type="text/coffeescript">
u = ABM.util # ABM.util alias, u.s is also ABM.shapes accessor.
log = (arg) -> console.log arg
class MyModel extends ABM.Model
@sdwfrost
sdwfrost / nyjazz.txt
Last active August 29, 2015 14:02
Processing RDS data
ID netsize mycoupon coupon1 coupon2 coupon3 coupon4 coupon5 coupon6 coupon7 coupon8 gender.mf age airplay.yn
1 350 0 14250004 14250005 14250006 14256002 0 0 0 901 1 40 1
2 0 0 14250007 14250008 14250009 14256003 0 0 912 902 1 64 1
3 585 0 14250010 14250011 14250012 14256004 0 0 0 903 2 41 1
4 400 0 14250025 14250026 14250027 14256009 0 0 0 904 2 77 0
5 150 0 14250022 14250023 14250023 14256008 0 0 0 905 1 33 1
6 100 0 14250028 14250029 14250030 14256010 0 0 916 906 1 31 2
7 300 0 14250016 14250017 14250018 14256006 0 0 0 907 1 70 1
8 700 0 14250040 14250041 14250042 14256014 0 0 0 908 1 49 1
9 300 14256002 14250013 14250014 14250015 14256005 0 0 0 909 2 38 1
@sdwfrost
sdwfrost / marglike.Rmd
Last active August 29, 2015 14:02
Marginal likelihoods via power posteriors and stepping stones in R
Under the Bayesian paradigm, inference is based on the posterior probability over the parameters of interest. It's helpful to think of our inferences being conditional on a given model, $M$ with a parameter vector $\theta \in \Theta$. Given a dataset, $D$, and a model, the posterior distribution of the parameter values is given by Bayes' theorem.
\[
\begin{align}
Pr(\theta|D,M) = \frac{Pr(D|\theta,M)Pr(\theta|M)}{Pr(D|M)}
\end{align}
\]
$Pr(D|\theta,M)$ is the likelihood function, $Pr(\theta|M)$ is the prior probability , and $Pr(D|M)$ is known as the marginal likelihood, predictive probability, or evidence. $Pr(D|M)$ is a normalising constant that ensures that $Pr(\theta|D,M)$ is a probability.
@sdwfrost
sdwfrost / marginal_likelihood_in_julia.ipynb
Created June 24, 2014 19:54
Marginal likelihood in Julia
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@sdwfrost
sdwfrost / hellobeagle.jl
Last active July 26, 2016 02:49
Example of using the BEAGLE library in Julia
@printf "BEAGLE version %s" bytestring(ccall((:beagleGetVersion,"libhmsbeagle"), Ptr{Cchar}, (),))
@printf "BEAGLE citation %s" bytestring(ccall((:beagleGetCitation,"libhmsbeagle"), Ptr{Cchar}, (),))
const BEAGLE_OP_COUNT = 7
const BEAGLE_OP_NONE = -1
immutable BeagleResource
name::Ptr{Uint8}
@sdwfrost
sdwfrost / hellobeagle.ipynb
Last active August 29, 2015 14:19
hellobeagle.jl as an IJulia notebook
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@sdwfrost
sdwfrost / vill.phy
Created May 2, 2015 16:48
Training datasets for PANGEA village simulations
This file has been truncated, but you can view the full file.
984 6987
HOUSE2308_11180 atgggtgcga gagcgtcaat attaagaagg ggaaaattag atacgtggga aaaaattagg
HOUSE1092_16633 atgggtgcga gagcgtcaat attaagaagg ggaaaaatag atacgtggga aaaaattagg
HOUSE3776_6969 atgggtgcga gagcgtcaat attaagaggg ggaaaattag atacatggga aaaaattaag
HOUSE660_14791 atgggtgcga gagcgtcaat attaaggtgg ggaaaactag atacatggga aaaaattaag
HOUSE1492_11556 atgggtgcga gagcgtcaat attaaggtgg ggaaaattag atacatggga aaaaattaag
HOUSE713_15113 atgggtgcga gagcgtcaat attaaggtgt ggaaaattag atacatggga aaaaattaag
HOUSE4256_16741 atgggtgcga gagcgtcaat attaaggtgt ggaaaattag atacatggga aaaaattaag
HOUSE2116_7615 atgggtgcga gagcgtcaat attaaggtgt ggaaaattag atacatggga aaaaattaag
HOUSE345_9899 atgggtgcaa gagcgtcaat attaagagga ggaaaattag atacatgtaa aaaaattagg
@sdwfrost
sdwfrost / hello.fas
Created May 5, 2015 09:56
Sequence file for use with hellobeagle.ipynb
>mars
CCGAG-AGCAGCAATGGAT-GAGGCATGGCG
>saturn
GCGCGCAGCTGCTGTAGATGGAGGCATGACG
>jupiter
GCGCGCAGCAGCTGTGGATGGAAGGATGACG