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In this post, I’m going to investigate the underlying relationships between various physical and mental health indicators and student stress levels. In the process I will give an introduction to the Uniform Manifold Approximation and Projection or UMAP dimensional reduction technique.
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In this post, I’ll give an practical introduction to flow matching for the sake of estimating complicated sample distributions and image generation.
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In this post, I’ll give an introduction to variational autoencoders, with some machine learning examples.
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In this post, I’ll attempt to give an introduction to simulation-based inference specifically delving into the method of NRE including rudimentary implementations.
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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.
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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.
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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.
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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.
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In this post I will attempt to give an introduction to binary classifiers and more generally neural networks.
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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
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In this post, I’ll attempt to give an introduction to normalising flows from the perspective of variational inference.
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In this post I will detail popular diagnostic tests to quantify how well/if your MCMC sampling has converged.
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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.
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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.
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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.
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Introduction into inverse transform sampling for continuous and discrete probability distributions.
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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.