# Gaussian Processes and Bayesian Inference

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/gaussian-processes-and-bayesian-inference/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of Gaussian Processes in machine learning, covering topics such as variational inference, sparse regression, Bayesian inference, deep learning, and probabilistic models. It also explores the use of Gaussian Processes for nonparametric methods, time series modelling, and handling big data. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t12814 |
| Works | 12 |

## Topic papers all

- [The neural hawkes process: a neurally self-modulating multivariate point process](https://scholariq.org/papers/the-neural-hawkes-process-a-neurally-self-modulating-multivariate-point-process-2/)
- [Random Sampling-High Dimensional Model Representation (RS-HDMR) and Orthogonality of Its Different Order Component Functions](https://scholariq.org/papers/random-sampling-high-dimensional-model-representation-rs-hdmr-and-orthogonality/)
- [Solution of physics-based Bayesian inverse problems with deep generative priors](https://scholariq.org/papers/solution-of-physics-based-bayesian-inverse-problems-with-deep-generative-priors/)
- [On the adaptation of recurrent neural networks for system identification](https://scholariq.org/papers/on-the-adaptation-of-recurrent-neural-networks-for-system-identification/)
- [Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics](https://scholariq.org/papers/probabilistic-safety-constraints-for-learned-high-relative-degree-system/)
- [A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows](https://scholariq.org/papers/a-dimension-reduced-variational-approach-for-solving-physics-based-inverse/)
- [GAN-Based Priors for Quantifying Uncertainty in Supervised Learning](https://scholariq.org/papers/gan-based-priors-for-quantifying-uncertainty-in-supervised-learning/)
- [Hierarchical probabilistic regression for AUV-based adaptive sampling of marine phenomena](https://scholariq.org/papers/hierarchical-probabilistic-regression-for-auv-based-adaptive-sampling-of-marine/)
- [Coherent Frameworks for Statistical Inference serving Integrating Decision Support Systems](https://scholariq.org/papers/coherent-frameworks-for-statistical-inference-serving-integrating-decision/)
- [Anchored Bayesian Gaussian mixture models](https://scholariq.org/papers/anchored-bayesian-gaussian-mixture-models/)
- [Learning robotic ultrasound scanning using probabilistic temporal ranking.](https://scholariq.org/papers/learning-robotic-ultrasound-scanning-using-probabilistic-temporal-ranking/)
- [Learning rewards for robotic ultrasound scanning using probabilistic temporal ranking](https://scholariq.org/papers/learning-rewards-for-robotic-ultrasound-scanning-using-probabilistic-temporal/)

## Topic primary papers

- [Solution of physics-based Bayesian inverse problems with deep generative priors](https://scholariq.org/papers/solution-of-physics-based-bayesian-inverse-problems-with-deep-generative-priors/)
- [On the adaptation of recurrent neural networks for system identification](https://scholariq.org/papers/on-the-adaptation-of-recurrent-neural-networks-for-system-identification/)
- [A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows](https://scholariq.org/papers/a-dimension-reduced-variational-approach-for-solving-physics-based-inverse/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.