## stats

### Efficient Gaussian Process Computation

I’ll try to give examples of efficient gaussian process computation here, like the vec trick (Kronecker product trick), efficient toeliptz and circulant matrix computations, RTS smoothing and Kalman filtering using state space representations, and so on.

### Sparse Gaussian Process Examples

## Minimal Working Examples

### Gaussian Process Speech Synthesis (Draft)

Very untidy first working draft of the idea mentioned on the efficient computation page. Here, I fit a spectral mixture to some audio data to build a “generative model” for audio. I’ll implement efficient sampling later, and I’ll replace the arbitrary way this is trained with an LSTM-RNN to go straight from text/spectrograms to waveforms.

### Random Projects

# Random Projects

### Gaussian Process Middle C

First of my experiments on audio modelling using gaussian processes. Here, I construct a GP that, when sampled, plays middle c the way a grand piano would.

### An Ising-Like Model

## … using Stan & HMC

### Random Stuff

## Random Stuff

### Stochastic Bernoulli Probabilities

Consider:

### Musings on Statistics

## Musings on Statistics

### Measures & Defining Probability

## Probability & Sigma Algebras

### Gaussian Processes

## Introduction

### The Log-Likelihood & Dimensionality

## Introduction

### Non-Monotonic Transforms

## Exact Densities for Non-Monotonic Transformations (draft)

## R

### Sparse Gaussian Process Examples

## Minimal Working Examples

### Gaussian Process Middle C

First of my experiments on audio modelling using gaussian processes. Here, I construct a GP that, when sampled, plays middle c the way a grand piano would.

### An Ising-Like Model

## … using Stan & HMC

### Gaussian Processes

## Introduction

### Non-Monotonic Transforms

## Exact Densities for Non-Monotonic Transformations (draft)

## python

### Efficient Gaussian Process Computation

I’ll try to give examples of efficient gaussian process computation here, like the vec trick (Kronecker product trick), efficient toeliptz and circulant matrix computations, RTS smoothing and Kalman filtering using state space representations, and so on.

### Gaussian Process Speech Synthesis (Draft)

Very untidy first working draft of the idea mentioned on the efficient computation page. Here, I fit a spectral mixture to some audio data to build a “generative model” for audio. I’ll implement efficient sampling later, and I’ll replace the arbitrary way this is trained with an LSTM-RNN to go straight from text/spectrograms to waveforms.

### Gaussian Processes

## Introduction

## c

### Efficient Gaussian Process Computation

I’ll try to give examples of efficient gaussian process computation here, like the vec trick (Kronecker product trick), efficient toeliptz and circulant matrix computations, RTS smoothing and Kalman filtering using state space representations, and so on.