Simon Frost sdwfrost

## ModelingWithJulia.ipynb

      
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                sdwfrost
                / ModelingWithJulia.ipynb
            
            
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              August 17, 2017 11:48
            
              
                Some examples of mathematical modeling and simple MCMC in Julia
              
          
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## ModelingWithR.ipynb

      
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                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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## sir_example.html
<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

## nyjazz.txt
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

## marglike.Rmd
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.

## marginal_likelihood_in_julia.ipynb

      
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                sdwfrost
                / marginal_likelihood_in_julia.ipynb
            
            
              Created
              June 24, 2014 19:54
            
              
                Marginal likelihood in Julia
              
          
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## hellobeagle.jl

@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}

## hellobeagle.ipynb

      
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                sdwfrost
                / hellobeagle.ipynb
            
            
              Last active
              August 29, 2015 14:19
            
              
                hellobeagle.jl as an IJulia notebook
              
          
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## vill.phy
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

## hello.fas
>mars
CCGAG-AGCAGCAATGGAT-GAGGCATGGCG
>saturn
GCGCGCAGCTGCTGTAGATGGAGGCATGACG
>jupiter
GCGCGCAGCAGCTGTGGATGGAAGGATGACG
	<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
	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
	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.

	@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}
	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
	>mars
	CCGAG-AGCAGCAATGGAT-GAGGCATGGCG
	>saturn
	GCGCGCAGCTGCTGTAGATGGAGGCATGACG
	>jupiter
	GCGCGCAGCAGCTGTGGATGGAAGGATGACG