Search Results for author: Christian Donner

Found 4 papers, 3 papers with code

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

3 code implementations23 May 2019 Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper

We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function.

Bayesian Inference Data Augmentation +2

Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

1 code implementation Journal of Machine Learning Research 2018 Christian Donner, Manfred Opper

We present an approximate Bayesian inference approach for estimating the intensity of an inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian process (GP) prior via a sigmoid link function.

Bayesian Inference

Inverse Ising problem in continuous time: A latent variable approach

1 code implementation4 Sep 2017 Christian Donner, Manfred Opper

For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

Bayesian Inference

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