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
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
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.
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. UNDER CONSTRUCTION
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
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
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?)
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. 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.
Variational 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. 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?)
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.