Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
An introduction to Simulation-Based Inference Part II: NRE
Published:
In this post, I’ll attempt to give an introduction to simulation-based inference specifically delving into the method of NRE including rudimentary implementations. UNDER CONSTRUCTION
An introduction to Simulation-Based Inference Part I: NPE and NLE
Published:
In this post, I’ll attempt to give an introduction to simulation-based inference specifically delving into the methods NPE and NLE including rudimentary implementations.
A RealNVP conditional normalising flow (from scratch?)
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In this post I will attempt to give an introduction to conditional normalising flows, not to be confused with continuous normalising flows, that model both \(\vec{\theta}\) and \(\vec{x}\) in the conditional distribution \(p(\vec{\theta}\vert\vec{x})\). I was nicely surprised at how simple it is to implement compared to unconditional normalising flows, so I thought I’d show this in a straightforward way. Assumes you’ve read my post on Building a normalising flow from scratch using PyTorch.
An introduction to continuous normalising flows
Published:
In this post I will attempt to give an introduction to continuous normalising flows, an evolution of normalising flows that translate the idea of training a discrete set of transformations to approximate a posterior, into training an ODE or vector field to do the same thing.
Building a normalising flow from scratch using PyTorch
Published:
In this post I will attempt to show you how to construct a simple normalising flow using base elements from PyTorch heavily inspired by a similar post by Eric Jang doing the same thing with TensorFlow from 2018 and subsequently his tutorial using JAX from 2019.
An introduction to binary classifiers with PyTorch
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In this post I will attempt to give an introduction to binary classifiers and more generally neural networks.
Variational Inference Introduction
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In this post I will attempt to give an introduction to variational inference with some examples using the NumPyro python package. Partly under construction
Normalising Flows for Variational Inference (with FlowJAX)
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In this post, I’ll attempt to give an introduction to normalising flows from the perspective of variational inference.
Markov Chain Monte Carlo convergence diagnostics
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In this post I will detail popular diagnostic tests to quantify how well/if your MCMC sampling has converged.
Markov Chain (+) Monte Carlo methods
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In this post I’ll go through “what is MCMC?”, “How is it useful for statistical inference?” And the conditions under which it is stable.
Practical Intro to the Metropolis-Hastings Algorithm/Fitting a line II
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In this post I’m going to try to give an intuitive intro into the Metropolis-Hastings algorithm without getting bogged down in much of the math to show the utility of this method.
Rejection Sampling
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In this post I’m going to introduce rejection sampling as a way to generate samples from an unnormalised pdf as further background to MCMC.
Inverse Transform Sampling
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Introduction into inverse transform sampling for continuous and discrete probability distributions.
First Blog Post/Fitting a line I
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First blog post, outlining what I’m going to try and do in the next few posts and some basics on Bayesian analysis.
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
publications
Online Machine-Learning-Based Event Selection for COMET Phase-I
Published in Phys.Sci.Forum, 2023
In this work I helped develop an initial framework to construct lightweight Convolutional Neural Networks to be loaded onto FPGA boards in the search for BSM signatures in COMET Phase-I data.
GammaBayes: a Bayesian pipeline for dark matter detection with CTA
Published in JCAP, 2024
This paper is about a Bayesian analysis package to analyse gamma ray event data to perform inference on dark matter model parameters.
Model-independent dark matter detection with the Cherenkov Telescope Array Observatory
Published in arXiv (pre-print), 2024
This paper is on how to perform indirect dark matter searches without presuming specifics on the dark matter decay or annihilation final states.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.