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Found 4 papers, 3 papers with code
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function.
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
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.
Efficient Bayesian Inference for a Gaussian Process Density Model
We reconsider a nonparametric density model based on Gaussian processes.
Inverse Ising problem in continuous time: A latent variable approach
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.
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