Between writing up my thesis, applying to jobs hire me! I’m quite good at programming, and the ongoing pandemic, I don’t really have time to write full blogposts. I have however decided to brush up my python skills and dive headfirst into Julia. As such, I like to answer the toy problems posted at fivethirtyeight’s riddler weekly. These will likely be even a few weeks late but it motivates me to tend to the blog and keep up my programming.
#for working with polygons library(sf) library(sfheaders) library(tidyverse) library(gtools) set.seed(22081992) Riddler Express This weeks express deals with an erratic driver:
In Riddler City, the city streets follow a grid layout, running north-south and east-west. You’re driving north when you decide to play a little game. Every time you reach an intersection, you randomly turn left or right, each with a 50 percent chance.
After driving through 10 intersections, what is the probability that you are still driving north?
Every Tuesday, the R4DataScience community posts a dataset online as part of #TidyTuesday as practice wrangling and modelling data. For the week of 5th May 2020, the dataset concerned the video game Animal Crossing.
Intro Radiohead - How I Made My Millions
I don’t play Animal Crossing (unfortunately Nintendo Switches sold out as the UK went into lockdown), but it seems that everyone around me does so I’ve become fascinated by how it has created almost a surrogate life for people, performing manual tasks to pay off loans to Tom Nook, the nefarious bankster of the player’s island.
I wrote this one pretty quickly compared to part 1 (which goes much deeper into mathematical concepts), and only realized after how much of a similarity it has to many of Ben Torvaney’s posts on the subject. This probably isn’t a coincidence given how much I’ve used his work previously in posts on this blog. Any imitation here is meant as flattery. The purpose of this post is really as a bridge between what I really want to write about- the maths behind the models in part 1, and extensions of these models into other distribution in parts 3-n so it might be a little derivative of stuff written elsewhere.
written during lockdown so while I think it adds some value (and is useful to organise my thoughts on the paper for my own work on football) there are probably mistakes. E.g. the C++ code is still pretty inefficient and could well be improved and I’ve surely confused some maths concepts. To be honest, the post is just an excuse to practice writing LaTeX maths and some C++. Let me know my errors and I’ll correct
generally as I have less and less time to waste on meaningless football stats I get halfway through a post and abandon it. To remedy this, I want to start pushing out posts that give a reasonable half-guess at an answer within an hour or so without needing to really check my working or write good prose. This is the third of these
For this weeks question, I’m stealing straight from the source of most of my posts, The Knowledge column at The Guardian: What is the shortest total distance a club has had to travel in a Champions League winning campaign?
generally as I have less and less time to waste on meaningless football stats I get halfway through a post and abandon it. To remedy this, I want to start pushing out posts that give a reasonable half-guess at an answer within an hour or so without needing to really check my working or write good prose. This is the second of these
A semi-common question I’ve come across when doing stupid football trivia is ‘Which Birthday could field the best 5-a-side team?
generally as I have less and less time to waste on meaningless football stats I get halfway through a post and abandon it. To remedy this, I want to start pushing out posts that give a reasonable half-guess at an answer within an hour or so without needing to really check my working or write good prose. This is the first of these
Liverpool Football Club have had a pretty impressive season until recently, winning 26 of the first 27 games and remaining unbeaten.
One of the most consistent fonts of posts on this blog is The Guardian’s football trivia page The Knowledge. A particular reason for this is that the small contained questions lend themselves to small blogposts that I can turn around in an hour or two, as opposed to being endlessly redrafted until I lose interest.
However, I still sometimes don’t quite get round to finishing some of these posts, or have trouble justifying a blog post on a very small and ‘trivial’ answer to a question.
I’m extremely biased, but to me, one of the real success* stories in neuroscience over the last (just over) two decades has been in studying reward signals. Since the seminal 1997 paper, a lot of work has gone into figuring out how the brain assigns value to outcomes.
*ugh, maybe. This isn’t a blog post about that
My PhD project looks at novel ways of eliciting valuation behaviour to study these signals, but as a key part of the modelling involved in this work, it’s important to get a handle on reinforcement learning.