Variational Inference Introduction
Published:
In this post I will attempt to give an introduction to variational inference with some examples using the NumPyro python package. Partly under construction
Posts by Tags
ADVI
BBVI
Variational Inference Introduction
Published:
In this post I will attempt to give an introduction to variational inference with some examples using the NumPyro python package. Partly under construction
Bayesian Analysis
Practical Intro to the Metropolis-Hastings Algorithm/Fitting a line II
Published:
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.
First Blog Post/Fitting a line I
Published:
First blog post, outlining what I’m going to try and do in the next few posts and some basics on Bayesian analysis.
CAVI
Variational Inference Introduction
Published:
In this post I will attempt to give an introduction to variational inference with some examples using the NumPyro python package. Partly under construction
Conditional Density Estimation
A RealNVP conditional normalising flow (from scratch?)
Published:
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.
Control Variates
Variational Inference Introduction
Published:
In this post I will attempt to give an introduction to variational inference with some examples using the NumPyro python package. Partly under construction
Dimensional Reduction
Investigating student stress indicators using UMAP (Kaggle Dataset)
Published:
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.
Flow Matching
Flow matching for multi-modal density estimation and image generation
Published:
In this post, I’ll give an practical introduction to flow matching for the sake of estimating complicated sample distributions and image generation.
FlowJAX
Normalising Flows for Variational Inference (with FlowJAX)
Published:
In this post, I’ll attempt to give an introduction to normalising flows from the perspective of variational inference.
Introductory
Investigating student stress indicators using UMAP (Kaggle Dataset)
Published:
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.
Markov Chain Monte Carlo convergence diagnostics
Published:
In this post I will detail popular diagnostic tests to quantify how well/if your MCMC sampling has converged.
Markov Chain (+) Monte Carlo methods
Published:
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
Published:
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
Published:
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
Published:
Introduction into inverse transform sampling for continuous and discrete probability distributions.
First Blog Post/Fitting a line I
Published:
First blog post, outlining what I’m going to try and do in the next few posts and some basics on Bayesian analysis.
JAX
Variational Inference Introduction
Published:
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)
Published:
In this post, I’ll attempt to give an introduction to normalising flows from the perspective of variational inference.
MCMC
Markov Chain Monte Carlo convergence diagnostics
Published:
In this post I will detail popular diagnostic tests to quantify how well/if your MCMC sampling has converged.
Markov Chain (+) Monte Carlo methods
Published:
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
Published:
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.
Machine Learning
Investigating student stress indicators using UMAP (Kaggle Dataset)
Published:
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.
NLE
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.
NPE
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.
NRE
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.
Neural Networks
An introduction to binary classifiers with PyTorch
Published:
In this post I will attempt to give an introduction to binary classifiers and more generally neural networks.
Normalising Flows
Flow matching for multi-modal density estimation and image generation
Published:
In this post, I’ll give an practical introduction to flow matching for the sake of estimating complicated sample distributions and image generation.
A RealNVP conditional normalising flow (from scratch?)
Published:
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.
Normalising Flows for Variational Inference (with FlowJAX)
Published:
In this post, I’ll attempt to give an introduction to normalising flows from the perspective of variational inference.
NumPyro
Variational Inference Introduction
Published:
In this post I will attempt to give an introduction to variational inference with some examples using the NumPyro python package. Partly under construction
PyTorch
Flow matching for multi-modal density estimation and image generation
Published:
In this post, I’ll give an practical introduction to flow matching for the sake of estimating complicated sample distributions and image generation.
Data Compression and generation with Variational Autoencoders
Published:
In this post, I’ll give an introduction to variational autoencoders, with some machine learning examples.
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.
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?)
Published:
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
Published:
In this post I will attempt to give an introduction to binary classifiers and more generally neural networks.
Pyro
A RealNVP conditional normalising flow (from scratch?)
Published:
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.
RealNVP
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.
Sampling Methods
Rejection Sampling
Published:
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
Published:
Introduction into inverse transform sampling for continuous and discrete probability distributions.
Simulation Based Inference
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.
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.
VAEs
Data Compression and generation with Variational Autoencoders
Published:
In this post, I’ll give an introduction to variational autoencoders, with some machine learning examples.
Variational Autoencoders
Data Compression and generation with Variational Autoencoders
Published:
In this post, I’ll give an introduction to variational autoencoders, with some machine learning examples.
Variational Inference
Flow matching for multi-modal density estimation and image generation
Published:
In this post, I’ll give an practical introduction to flow matching for the sake of estimating complicated sample distributions and image generation.
Data Compression and generation with Variational Autoencoders
Published:
In this post, I’ll give an introduction to variational autoencoders, with some machine learning examples.
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.
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?)
Published:
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.
Variational Inference Introduction
Published:
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)
Published:
In this post, I’ll attempt to give an introduction to normalising flows from the perspective of variational inference.