SaranjeetKaur

Introduction

In this Google Summer of Code program I, along with my mentors, aimed to build the polychord nested sampling algorithm and integrate it with Turing using Julia Language. The version 0.5.0 release of NestedSamplers.jl has our work done, to date, included. This would allow users to use the random staggering, slicing and random slicing (or polychord) proposal alogrithms in NestedSamplers.jl. Much of this work was inspired by dynesty and its modular approach to nested sampling, which Julia’s multiple dispatch made even more effective. The majority of the code for the proposal algorithms has already been merged to NestedSamplers.jl. One of the major improvements which would greatly increase its usage would be merging ns.jl with Turing.jl package.

Blog post

The work done in this project is described in this [b

SaranjeetKaur

julia> using NestedSamplers
julia> using Distributions

# eggbox likelihood function
tmax = 3π
julia> function logl(x)
       t = @. 2*tmax*x - tmax
       return 2 + cos(t[1]/2)*cos(t[2]/2)^5
       end
logl (generic function with 1 method)

SaranjeetKaur

install.packages("tidyverse")
library("tidyverse")

attendance <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-04/attendance.csv')
standings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-04/standings.csv')
games <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-04/games.csv')

attendance <- na.omit(attendance)
attendance_arizona <- subset(attendance, attendance$team == "Arizona")

SaranjeetKaur

install.packages("tidyverse")
library(tidyverse)

trees_SF <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-28/sf_trees.csv')
trees_SF <- na.omit(trees_SF)
trees_map_SF <- ggplot2::ggplot(data = trees_SF, aes(x = longitude, y = latitude, colour = caretaker)) +
  geom_point() +
  labs(x = 'Longitude', y = 'Latitude', title = 'Trees in San Franscisco & their caretakers') +
  xlim(min(trees_SF$longitude), max(trees_SF$longitude)) +
  ylim(min(trees_SF$latitude), max(trees_SF$latitude))

SaranjeetKaur

install.packages("tidyverse")
library(tidyverse)
spotify <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv')


acoustics <- ggplot2::ggplot(data = spotify) +
  geom_point(mapping = aes(x = 1:nrow(spotify), y = acousticness, colour = playlist_genre)) + 
  labs(x = 'spotify songs')
acoustics