Stucchio stucchio

stucchio / basic_income_with_death_estimate.py

Created November 15, 2013 20:51

Same as https://gist.github.com/stucchio/7447067 but accounting for death rates, as suggested by a commenter on the blog.

	from pylab import *
	from scipy.stats import *

	num_adults = 227e6
	basic_income = 7.254050
	labor_force = 154e6
	disabled_adults = 21e6
	current_wealth_transfers = 3369e9

	def jk_rowling(num_non_workers):

stucchio / jacob_diffusion.py

Last active February 16, 2019 22:04

Code for the post http://www.chrisstucchio.com/blog/2013/time_varying_conversion_rates.html

	from pylab import *
	from numpy import *
	from numpy.linalg import solve
	from scipy.integrate import odeint
	from scipy.stats import norm, uniform, beta
	from scipy.special import jacobi

	a = 0.0
	b = 3.0
	theta=1.0

stucchio / jacobi_diffusion_updating_evidence.py

Created November 21, 2013 09:25

	from pylab import *
	from numpy import *
	from numpy.linalg import solve
	from scipy.integrate import odeint
	from scipy.stats import norm, uniform, beta
	from scipy.special import jacobi



	a = 0.0

stucchio / simple_mortgage.py

Last active October 30, 2015 22:11

For a forthcoming blog post.

	from pylab import *
	from scipy.stats import uniform

	gamma = 0.01/12.0
	t = arange(0,360)

	def compute_payout(payout):
	return (payout * exp( - gamma * t)).sum()

	def generate_payout():

stucchio / portfolio.py

Created February 6, 2014 04:46

	from pylab import *
	from scipy.stats import uniform

	gamma = 0.01/12.0
	t = arange(0,360)

	num_mortgages=50

	def compute_payout(payout):
	return (payout * exp( - gamma * t)).sum()

stucchio / 3sample_bandit.py

Created February 6, 2014 07:44

	from pylab import *
	import random
	from scipy.stats import beta, uniform

	prior = beta(1,1)

	class Bandit(object):
	def __init__(self):
	self.history = [(1.0,1.0), (1.0,1.0)]

stucchio / bandit_8020_comparison.py

Created February 7, 2014 18:55

	import matplotlib
	matplotlib.use("WXAgg")
	from pylab import *
	from scipy.stats import beta, uniform, norm

	class BetaBandit(object):
	def __init__(self, num_options=2, prior=(1.0,1.0)):
	self.trials = zeros(shape=(num_options,), dtype=int)
	self.successes = zeros(shape=(num_options,), dtype=int)
	self.num_options = num_options

stucchio / bayesian_ab_test.py

Last active April 2, 2023 03:17

Bayesian A/B test code

	from matplotlib import use

	from pylab import *
	from scipy.stats import beta, norm, uniform
	from random import random
	from numpy import *
	import numpy as np
	import os

	# Input data

stucchio / bayesian_ab_test_empiritcs.py

Last active January 26, 2021 00:54

bayesian a/b test empirics


	from pylab import *
	from scipy.stats import beta, norm, uniform
	from random import random
	from numpy import *
	import numpy as np
	import os

	# Input data
	prior_params = [ (1, 1), (1,1) ]

stucchio / mle_compute_z.jl

Last active August 29, 2015 14:01

Maximum likelihood computation of ad probability

	#Pkg.add("Optim")
	using Optim

	function logLikelihood(z::Float64, clicks::Array{Float64,1}, shows::Array{Float64,1}, alpha::Array{Float64,1})
	@assert size(clicks) == size(shows)
	@assert size(shows) == size(alpha)
	az = z * alpha
	return sum(clicks .* log(az) .+ (shows .- clicks) .* log(1-az))
	end