Gaussian Processes and Bayesian Inference

Neural Processes with Normalizing Flows for Wheat Height Estimation

This paper uses neural processes with normalizing flows to model wheat height trajectories, evaluating their ability to capture distributional dynamics influenced by genotype and environment.

MOFTy: Multimodal Gaussian Process Factor Analysis with Numerical Information Field Theory

MOFTy disentangles spatial structures and recovers cross-modal patterns in multi-omics data by separating smooth gradients from fine-scale heterogeneity.

On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing

The paper establishes the relationship between Gaussian process priors and regularized regression in sequence-to-function mapping, showing how to construct regularizers that correspond to explicit Gaussian process priors and characterize implicit function space priors.

waveome: a toolkit for longitudinal omics analysis using Gaussian processes

The paper introduces waveome, a Python toolkit using Gaussian processes for longitudinal omics analysis, identifying biomarkers in Crohn's disease with sparse, irregular data.

Gaussian process forecasting of sparse ecological time series

This paper forecasts unevenly sampled ecological time series using Gaussian process models, outperforming linear regression on tick abundance data.

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA

The paper introduces an infinite GPFA model to infer dynamically changing latent factors in neural data, showing it captures meaningful dynamics in hippocampal place cells during spatial tasks.

Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression

This paper introduces a Gaussian process multi-class decoder (GPMD) for neural decoding, achieving state-of-the-art accuracy across species and demonstrating the importance of correlation structure.

Variational Log-Gaussian Point-Process Methods for Grid Cells

The paper develops variational log-Gaussian point-process methods to efficiently calculate spatial statistics for grid cells in large environments using Gaussian processes.

An amortized approach to non-linear mixed-effects modeling based on neural posterior estimation

The paper introduces a machine learning approach to non-linear mixed-effects modeling using neural density estimation, demonstrating its flexibility and scalability in cell biology and pharmacology.

Scalable gaussian process inference of neural responses to natural images

This paper develops a scalable Gaussian process model for predicting neural responses to natural images, outperforming deep neural networks on small datasets and enabling efficient model learning and comparison.

Scalable variational inference for low-rank spatio-temporal receptive fields

The paper introduces a scalable variational inference method for estimating low-rank spatio-temporal receptive fields in neural data, showing improved performance over existing estimators.

Warped Bayesian Linear Regression for Normative Modelling of Big Data

This paper introduces a warped Bayesian Linear Regression framework for normative modelling, improving scalability and non-Gaussian distribution handling in neuroimaging data.

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data

The paper introduces iLQR-VAE, a control-based variational inference method for learning dynamics and inputs from neural data, showing effectiveness on synthetic and primate reaching task data.

Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories

The paper develops hierarchical Gaussian processes and mixture of experts models to analyze EHR data from COVID-19 patients, showing improved predictive performance for clinical markers like albumin.

The pitfalls of using Gaussian Process Regression for normative modeling

This paper shows that Gaussian Process Regression uncertainty is not relevant for quantifying cohort heterogeneity in normative modeling.

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models

This paper introduces a scalable Bayesian Gaussian process factor analysis (bGPFA) for neural data, showing it captures preparatory dynamics and long-timescale processes in primate motor cortex.

Similarity Measure for Sparse Time Course Data Based on Gaussian Processes

The paper introduces a similarity measure for sparse time course data using Gaussian processes, showing improved clustering performance on synthetic and real data.

Identifying signal and noise structure in neuralpopulation activity with Gaussian process factormodels

This paper introduces SNP-GPFA, a Gaussian process factor model to separate signal and noise in neural population activity, showing noise perturbs activity orthogonal to signal.

Bayesian Hypothesis Testing And Experimental Design For Two-Photon Imaging Data

The paper develops a Bayesian statistical pipeline for analyzing two-photon imaging data using Gaussian Process Regression to infer neural activity and guide experimental design.

Branch-recombinant Gaussian processes for analysis of perturbations in biological time series

This paper introduces branch-recombinant Gaussian processes (B-RGPs) to analyze branching and recombination in biological time series, demonstrating their application to gene expression data in Arabidopsis thaliana.

Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

The paper introduces two nonparametric Bayesian methods for estimating spike-spike and spike-membrane neuronal connectivity using Gaussian process regression and validates them on simulated and real data.

Temporal alignment and latent Gaussian process factor inference in population spike trains

This paper introduces a scalable method for analyzing neural population spike trains by extending Gaussian Process Factor Analysis to handle continuous time data and capture trial-to-trial variability.

LonGP: an additive Gaussian process regression model for longitudinal study designs

LonGP is a Gaussian process regression model for longitudinal studies that handles time-varying covariates and non-linear effects, showing performance on simulated and real -omics data.

Scalable Bayesian inference for high-dimensional neural receptive fields

The paper develops scalable Bayesian methods for estimating high-dimensional neural receptive fields, achieving fast and memory-efficient inference.