notes
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index
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Gaussian process
software
Stheno.jl
JuliaCon 2019 | Gaussian Process Probabilistic Programming with Stheno.jl | Will Tebbutt * YouTube
JuliaCon 2020 | Fast Gaussian processes for time series | Will Tebbutt * YouTube
https://discourse.julialang.org/t/gaussian-process-model-with-turing/42453/13
references
Gaussian processes for time-series modelling by Roberts, Osborne, Aigrain, et al in 2013
Clustering gene expression time series data using an infinite Gaussian process mixture model by Ian McDowell, Timothy Reddy, Barbara Engelhardt, et al in 2018
Campbell, K., & Yau, C. (2015). Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data.
https://doi.org/10.1101/026872
Bayesian non-parametrics and the probabilistic approach to modelling by Zoubin Ghahramani in 2013
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters by James Hensman, Neil Lawrence, and Magnus Rattray in 2013
Links to this note
open documents
approximate bayesian computation
turing.jl
Stheno.jl
Michael P. H. Stumpf
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters by James Hensman, Neil Lawrence, and Magnus Rattray in 2013
GpABC a Julia package for approximate bayesian computation with Gaussian process emulation by Evgeny Tankhilevich, Michael P. H. Stumpf et al in 2020
Gaussian processes for time-series modelling by Roberts, Osborne, Aigrain, et al in 2013
DPMM-GP model
Clustering gene expression time series data using an infinite Gaussian process mixture model by Ian McDowell, Timothy Reddy, Barbara Engelhardt, et al in 2018