Search Results for author:
Found 8 papers, 5 papers with code
High-dimensional additive Gaussian processes under monotonicity constraints
First, we show that our framework enables to satisfy the constraints everywhere in the input space.
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
We introduce a novel finite approximation of GP-modulated Cox processes where positiveness conditions can be imposed directly on the GP, with no restrictions on the covariance function.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Finally, on 2D and 5D coastal flooding applications, we show that more flexible and realistic GP implementations can be obtained by considering noise effects and by enforcing the (linear) inequality constraints.
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
Since the post-transcriptional regulation of Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the link between both quantities.
Maximum likelihood estimation for Gaussian processes under inequality constraints
We first show that the (unconstrained) maximum likelihood estimator has the same asymptotic distribution, unconditionally and conditionally, to the fact that the Gaussian process satisfies the inequality constraints.
Statistics Theory Probability Statistics Theory
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems.
Switched latent force models for reverse-engineering transcriptional regulation in gene expression data
To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations.
Sparse Linear Models applied to Power Quality Disturbance Classification
The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features.
