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thomaspinder / control_variates.ipynb
Last active Jul 11, 2019
Control variates for Monte-Carlo integration in Julia
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thomaspinder / bbvi-julia.jl
Created Feb 11, 2019
Black-Box Variational Inference in Julia (Work in Progress!), based upon
View bbvi-julia.jl
using Distributions, Random, LinearAlgebra, Plots, StatPlots
dims = 2
precision_prior = 4
cov_prior = Matrix{Float64}(I, dims, dims)
pre_prior = precision_prior*cov_prior
mu_prior = [-2, -4]
precision_t = 0.5
thomaspinder / big_o_complexity.R
Created Jan 15, 2019
Using gganimate to visualise the chainging computational complexity as n increases.
View big_o_complexity.R
# Get base n values
n <- 50
base <- data.frame(idx = seq(2, n, by = 0.01))
# Compute relative runtimes
If you place this script at the bottom of your Python work, then it'll send you a notification on Slack once your code has finished running. Potentially useful if your code takes several hours to run as it saves having to check on its progress.
To run, you'll need to install the following two libraries:
And then sign up for a Slack API key. Once these two steps have been done you can run the following code, replacing the name and key values with your own.
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