Saved in:
| Main Authors: | Rosato, Conor, Murphy, Joshua, Varsi, Alessandro, Horridge, Paul, Maskell, Simon |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.17296 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improved Disease Outbreak Detection from Out-of-sequence measurements Using Markov-switching Fixed-lag Particle Filters
by: Rosato, Conor, et al.
Published: (2025)
by: Rosato, Conor, et al.
Published: (2025)
Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
by: Murphy, Joshua, et al.
Published: (2025)
by: Murphy, Joshua, et al.
Published: (2025)
Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
by: Rosato, Conor, et al.
Published: (2025)
by: Rosato, Conor, et al.
Published: (2025)
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
by: Millard, Andrew, et al.
Published: (2025)
by: Millard, Andrew, et al.
Published: (2025)
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
by: Rosato, Conor, et al.
Published: (2025)
by: Rosato, Conor, et al.
Published: (2025)
Enhancing Gradient-based Discrete Sampling via Parallel Tempering
by: Liang, Luxu, et al.
Published: (2025)
by: Liang, Luxu, et al.
Published: (2025)
Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework
by: Zhang, Dashan, et al.
Published: (2023)
by: Zhang, Dashan, et al.
Published: (2023)
Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks
by: Millard, Andrew, et al.
Published: (2026)
by: Millard, Andrew, et al.
Published: (2026)
Understanding When Graph Convolutional Networks Help: A Diagnostic Study on Label Scarcity and Structural Properties
by: Subedi, Nischal, et al.
Published: (2025)
by: Subedi, Nischal, et al.
Published: (2025)
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
by: Millard, Andrew, et al.
Published: (2025)
by: Millard, Andrew, et al.
Published: (2025)
AI and analytics in sports: Leveraging BERTopic to map the past and chart the future
by: Mishra, Manit
Published: (2025)
by: Mishra, Manit
Published: (2025)
Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques
by: Park, Yoon Tae, et al.
Published: (2024)
by: Park, Yoon Tae, et al.
Published: (2024)
Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach
by: Farha, Ramisa, et al.
Published: (2025)
by: Farha, Ramisa, et al.
Published: (2025)
Differentially private synthesis of Spatial Point Processes
by: Kim, Dangchan, et al.
Published: (2025)
by: Kim, Dangchan, et al.
Published: (2025)
Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2025)
by: Ghasemloo, Mohammadmahdi, et al.
Published: (2025)
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
by: Millard, Andrew, et al.
Published: (2025)
by: Millard, Andrew, et al.
Published: (2025)
Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators
by: Miller, John Joshua, et al.
Published: (2024)
by: Miller, John Joshua, et al.
Published: (2024)
An Explainable AI Model for Predicting the Recurrence of Differentiated Thyroid Cancer
by: Ahmad, Mohammad Al-Sayed, et al.
Published: (2024)
by: Ahmad, Mohammad Al-Sayed, et al.
Published: (2024)
Dyadic Reinforcement Learning
by: Li, Shuangning, et al.
Published: (2023)
by: Li, Shuangning, et al.
Published: (2023)
Statistical Reinforcement Learning in the Real World: A Survey of Challenges and Future Directions
by: Gazi, Asim H., et al.
Published: (2026)
by: Gazi, Asim H., et al.
Published: (2026)
Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimation
by: Zhou, Biying, et al.
Published: (2025)
by: Zhou, Biying, et al.
Published: (2025)
Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data
by: Goh, Yong Chen, et al.
Published: (2024)
by: Goh, Yong Chen, et al.
Published: (2024)
The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes
by: von der Maase, Simon P.
Published: (2025)
by: von der Maase, Simon P.
Published: (2025)
Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression
by: Shao, Sisi, et al.
Published: (2024)
by: Shao, Sisi, et al.
Published: (2024)
Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making
by: Castelnovo, Alessandro
Published: (2024)
by: Castelnovo, Alessandro
Published: (2024)
Leveraging generative adversarial networks with spatially adaptive denormalization for multivariate stochastic seismic data inversion
by: Miele, Roberto, et al.
Published: (2025)
by: Miele, Roberto, et al.
Published: (2025)
The Living Forecast: Evolving Day-Ahead Predictions into Intraday Reality
by: Bölat, Kutay, et al.
Published: (2025)
by: Bölat, Kutay, et al.
Published: (2025)
Modelling non-stationary extremal dependence through a geometric approach
by: Murphy-Barltrop, C. J. R., et al.
Published: (2025)
by: Murphy-Barltrop, C. J. R., et al.
Published: (2025)
In-Context Learning Enhanced Credibility Transformer
by: Padayachy, Kishan, et al.
Published: (2025)
by: Padayachy, Kishan, et al.
Published: (2025)
Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
by: Lu, Tianlu, et al.
Published: (2026)
by: Lu, Tianlu, et al.
Published: (2026)
Bayesian Event Categorization Matrix Approach for Explosion Monitoring
by: Koermer, Scott, et al.
Published: (2024)
by: Koermer, Scott, et al.
Published: (2024)
Enhancing Interpretability and Generalizability in Extended Isolation Forests
by: Arcudi, Alessio, et al.
Published: (2023)
by: Arcudi, Alessio, et al.
Published: (2023)
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
by: Meng, Xianghao, et al.
Published: (2026)
by: Meng, Xianghao, et al.
Published: (2026)
The No-U-Turn Sampler as a Proposal Distribution in a Sequential Monte Carlo Sampler with a Near-Optimal L-Kernel
by: Devlin, Lee, et al.
Published: (2021)
by: Devlin, Lee, et al.
Published: (2021)
Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction
by: Moradi, Alireza, et al.
Published: (2025)
by: Moradi, Alireza, et al.
Published: (2025)
Enhancing multivariate post-processed visibility predictions utilizing CAMS forecasts
by: Lakatos, Mária, et al.
Published: (2024)
by: Lakatos, Mária, et al.
Published: (2024)
Concave Statistical Utility Maximization Bandits via Influence-Function Gradients
by: Carrasco, Matías, et al.
Published: (2026)
by: Carrasco, Matías, et al.
Published: (2026)
Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
by: Wang, Yinsong, et al.
Published: (2025)
by: Wang, Yinsong, et al.
Published: (2025)
DemandLens: Enhancing Forecast Accuracy Through Product-Specific Hyperparameter Optimization
by: Pillai, Srijesh, et al.
Published: (2025)
by: Pillai, Srijesh, et al.
Published: (2025)
Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
by: Hu, Annie, et al.
Published: (2024)
by: Hu, Annie, et al.
Published: (2024)
Similar Items
-
Improved Disease Outbreak Detection from Out-of-sequence measurements Using Markov-switching Fixed-lag Particle Filters
by: Rosato, Conor, et al.
Published: (2025) -
Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
by: Murphy, Joshua, et al.
Published: (2025) -
Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
by: Rosato, Conor, et al.
Published: (2025) -
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
by: Millard, Andrew, et al.
Published: (2025) -
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
by: Rosato, Conor, et al.
Published: (2025)