List of Neil's Publications.
2026
Journal Papers
Mind the gap: connecting AI innovation to widespread public value
Science and Public Policy, :
Technical Reports
The No Barber Principle: Towards Formalised Selection in the Inaccessible Game
:
On the Value of Tokeniser Pretraining in Physics Foundation Models
:
The Origin of the Inaccessible Game
:
Optimising for Energy Efficiency and Performance in Machine Learning
:
2025
Journal Papers
Machine Learning Systems: A Survey from a Data-Oriented Perspective
ACM Computing Surveys, :
Conference Papers
Code Roulette: How Prompt Variability Affects LLM Code Generation
Third International Workshop on Large Language Models for Code (LLM4Code @ ICSE), :
Technical Reports
The Inaccessible Game
:
Beyond Quantification: Navigating Uncertainty in Professional AI Systems
:
Revisiting Deep AC-OPF
:
Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements
:
Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout
:
LLM Performance for Code Generation on Noisy Tasks
:
The Human Visual System Can Inspire New Interaction Paradigms for LLMs
:
2024
Books
The Atomic Human: Understanding ourselves in the age of AI
Allen Lane:
Journal Papers
Increasing data sharing and use for social good: Lessons from Africa’s data-sharing practices during the COVID-19 response
Data & Policy, 6:
Accelerating AI for science: open data science for science
Royal Society Open Science, 11(8):
Requirements are All You Need: The Final Frontier for End-User Software Engineering
ACM Transactions on Software Engineering and Methodology, :
Conference Papers
The Systems Engineering Approach in Times of Large Language Models
58th Hawaii International Conference on System Sciences (HICSS-58), :
Can causality accelerate experimentation in software systems?
Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering, :
Self-sustaining software systems (S4): Towards improved interpretability and adaptation
Proceedings of the 1st International Workshop on New Trends in Software Engineering, :
Technical Reports
On Feature Learning for Titi Monkey Activity Detection
:
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
:
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
:
Enhancing patient stratification and interpretability through class-contrastive and feature attribution techniques
:
2023
Conference Papers
Automated discovery of trade-off between utility, privacy and fairness in machine learning models
3rd Workshop on Bias and Fairness in AI (BIAS), ECML 2023, :
Causal fault localisation in dataflow systems
Proceedings of the 3rd Workshop on Machine Learning and Systems (EuroMLSys), :
Dataflow graphs as complete causal graphs
2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering Approaches, :
Technical Reports
Multi-fidelity experimental design for ice-sheet simulation
:
Predicting Ruthenium Catalysed Hydrogenation of Esters Using Machine Learning
Digital Discovery, RSC 2:
Dimensionality Reduction as Probabilistic Inference
:
AI for Science: an emerging agenda
:
2022
Journal Papers
Bayesian learning via neural Schrödinger–Föllmer flows
Statistics and Computing, 33(3):
Challenges in Machine Learning Deployment: A Survey of Case Studies
ACM Comput. Surv., Association for Computing Machinery:
Conference Papers
Modeling the Machine Learning Multiverse
Advances in Neural Information Processing Systems (NeurIPS), :
An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context
Proceedings of the 1st International Conference on AI Engineering — Software Engineering for AI (CAIN), :
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), :
Technical Reports
Ice Core Dating using Probabilistic Programming
:
Modelling Technical and Biological Effects in scRNA-seq Data with Scalable GPLVMs
:
The Effect of Task Ordering in Continual Learning
:
Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data
:
2021
Journal Papers
Differentially Private Regression and Classification with Sparse Gaussian Processes
Journal of Machine Learning Research, 22(188):1-41
Solving Schrödinger Bridges via Maximum Likelihood
Entropy, 23(9):1134
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Journal of Machine Learning Research, 22(8):1-51
Technical Reports
Emulation of physical processes with Emukit
:
Behavioral Experiments for Understanding Catastrophic Forgetting
:
Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment
:
Towards better data discovery and collection with flow-based programming
:
2020
Journal Papers
Decision-making with Uncertainty
Significance, 17(6):12-12
Conference Papers
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
International Conference on Learning Representations, :
2019
Journal Papers
Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance
International Data Privacy Law, Oxford Academic 9(4):236-252
Intrinsic Gaussian Processes on Complex Constrained Domains
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(3):
Conference Papers
Variational Information Distillation for Knowledge Transfer
Conference on Computer Vision and Pattern Recognition (CVPR), :9155-9163
Transferring Knowledge across Learning Processes
International Conference on Learning Representations, :
Technical Reports
Meta-Surrogate Benchmarking for Hyperparameter Optimization
:
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
:
Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design
:
Continual Learning in Practice
:
2018
Journal Papers
Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
IEEE Transactions on Automatic Control, IEEE 64(7):2953-2960
The Emergence of Organizing Structure in Conceptual Representation
Cognitive Science, 42(S3):1-24
Conference Papers
Structured Variationally Auto-encoded Optimization
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:3273-3281
Differentially Private Regression with Gaussian Processes
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:1195-1203
Technical Reports
Gaussian Process Regression for Binned Data
:
Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS
:
2017
Journal Papers
Efficient Inference for Sparse Latent Variable Models of Transcriptional Regulation
Bioinformatics, 23:3776-3783
Conference Papers
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
Advances in Neural Information Processing Systems, Curran Associates, Inc. 30:5131-5139
Preferential Bayesian Optimization
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1282-1291
Technical Reports
Auto-Differentiating Linear Algebra
:
Living Together: Mind and Machine Intelligence
:
Data Readiness Levels
:
Manifold Alignment Determination: finding correspondences across different data views
:
2016
Journal Papers
Detecting Periodicities with Gaussian processes
PeerJ Computer Science, 4:e50
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes
Journal of Machine Learning Research, 17(42):1-62
Conference Papers
Chained Gaussian Processes
Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:1431-1440
Recurrent Gaussian Processes
Proceedings of the International Conference on Learning Representations, 3:
GLASSES: Relieving The Myopia Of Bayesian Optimisation
Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:790-799
Variationally Auto-Encoded Deep Gaussian Processes
Proceedings of the International Conference on Learning Representations, 3:
Batch Bayesian Optimization via Local Penalization
Proceedings of the Nineteenth International Workshop on Artificial Intelligence and Statistics, PMLR 51:648-657
Technical Reports
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)
:
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology
:
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
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Topslam: Waddington Landscape Recovery for Single Cell Experiments
:
Differentially Private Gaussian Processes
:
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
:
2015
Journal Papers
Genome-wide Modeling of Transcription Kinetics Reveals Patterns of RNA Production Delays
Proc. Natl. Acad. Sci. USA, 112(42):13115-13120
A Reverse-Engineering Approach to Dissect Post-translational Modulators of transcription Factor's Activity from Transcriptional Data
BMC Bioinformatics, 16(279):
Conference Papers
Semi-described and Semi-supervised Learning with Gaussian Processes
31st Conference on Uncertainty in Artificial Intelligence (UAI), :
Technical Reports
Spike and Slab Gaussian Process Latent Variable Models
:
Bayesian Optimization for Synthetic Gene Design
:
2014
Journal Papers
Consistent Mapping of Government Malaria Records Across a Changing Territory Delimitation
Malaria Journal, 13(Suppl 1):
Warped Linear Mixed Models for the Genetic Analysis of Transformed Phenotypes
Nature Communications, 5(4890):
Inference of RNA Polymerase II Transcription Dynamics from Chromatin Immunoprecipitation Time Course Data
PLoS Computat Biol, 10(5):
Fast Nonparametric Clustering of Structured Time-Series
IEEE Transactions on Pattern Analysis and Machine Intelligence, 37:383-393
Conference Papers
Malaria surveillance with multiple data sources using Gaussian process models
1st International Conference on the Use of Mobile ICT in Africa, :
Metrics for Probabilistic Geometries
Uncertainty in Artificial Intelligence, AUAI Press 30:800-808
Tilted Variational Bayes
Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics, PMLR 33:356-364
Hybrid Discriminative-Generative Approaches with Gaussian Processes
Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics, PMLR 33:47-56
Technical Reports
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
:
Nested Variational Compression in Deep Gaussian Processes
:
Miscellaneous
Gaussian Process Models with Parallelization and GPU acceleration
:
2013
Journal Papers
Hierarchical Bayesian Modelling of Gene Expression Time Series Across Irregularly Sampled Replicates and Clusters
BMC Bioinformatics, 14(252):
Linear Latent Force Models Using Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11):2693-2705
Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity
Acta Neuropathologica, 125(1):
Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors
Bioinformatics, 29(11):1382-1389
Conference Papers
Gaussian Processes for Big Data
Uncertainty in Artificial Intelligence, AUAI Press 29:
The Bigraphical Lasso
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1229-1237
Deep Gaussian Processes
Proceedings of the Sixteenth International Workshop on Artificial Intelligence and Statistics, PMLR 31:207-215
2012
Journal Papers
Kernels for Vector-Valued Functions: A Review
Foundations and Trends in Machine Learning, 4(3):195-266
Modeling Meiotic Chromosomes Indicates a Size Dependent Contribution of Telomere Clustering and Chromosome Rigidity to Homologue Juxtaposition
PLoS Computational Biology, 8(5):0-0
Overlapping Mixtures of Gaussian Processes for the Data Association Problem
Pattern Recognition, 10(4):
Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies
PLoS Computat Biol, Public Library of Science 8:0-0
Genome-wide occupancy links Hoxa2 to Wnt-$\beta$-catenin signaling in mouse embryonic development
Nucleaic Acids Research, 40(9):3390-4001
Identifying Targets of Multiple Co-regulated Transcription Factors from Expression Time-series by Bayesian Model Comparison
BMC Systems Biology, 6(53):
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models
Journal of Machine Learning Research, 13(51):1609-1638
Conference Papers
Fast variational inference in the Conjugate Exponential family
Advances in Neural Information Processing Systems, 25:
Manifold Relevance Determination
Proceedings of the International Conference in Machine Learning, Morgan Kauffman 29:
Residual Component Analysis
Proceedings of the International Conference in Machine Learning, Morgan Kauffman 29:
Book Chapters
Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes Using Time Series Expression Data
Data Mining for Systems Biology, Springer-Verlag:
Technical Reports
Gaussian Processes for Big Data with Stochastic Variational Inference
Submitted to NIPS 2012 Workshop, :
2011
Journal Papers
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
BMC Bioinformatics, 12(180):
Computationally Efficient Convolved Multiple Output Gaussian Processes
Journal of Machine Learning Research, 12:1425-1466
tigre: Transcription Factor Inference through Gaussian Process Reconstruction of Expression for Bioconductor
Bioinformatics, 27:1026-1027
Conference Papers
Efficient Inference in Matrix-Variate Gaussian Models with i.i.d. Observation Noise
Neural Information Processing Systems, :630-638
Spectral Dimensionality Reduction via Maximum Entropy
Proceedings of the Fourteenth International Workshop on Artificial Intelligence and Statistics, PMLR 15:51-59
Variational Gaussian Process Dynamical Systems
Advances in Neural Information Processing Systems, MIT Press 24:
Book Chapters
Markov Chain Monte Carlo Algorithms for Gaussian Processes
Bayesian Time Series Models, Cambridge University Press:
Gaussian Process Inference for Differential Equation Models of Transcriptional Regulation
Handbook of Statistical Systems Biology, John Wiley and Sons:376-394
Technical Reports
Linear Latent Force Models Using Gaussian Processes
:
Kernels for Vector-Valued Functions: a Review
:
Residual Component Analysis
:
Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects
:
2010
Journal Papers
TFInfer: a tool for probabilistic inference of transcription factor activities
Bioinformatics, 26:2635-2636
Model-based Method for Transcription Factor Target Identification with Limited Data
Proc. Natl. Acad. Sci. USA, 107(17):7793-7798
Elementary properties of CaV1.3 Ca2+ channels expressed in mouse cochlear inner hair cells
The Journal of Physiology, 588:187-189
Conference Papers
Bayesian Gaussian Process Latent Variable Model
Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, PMLR 9:844-851
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
Proceedings of the Thirteenth International Workshop on Artificial Intelligence and Statistics, PMLR 9:25-32
Switched Latent Force Models for Movement Segmentation
Advances in Neural Information Processing Systems, MIT Press 23:55-63
Book Chapters
Introduction to Learning and Inference in Computational Systems Biology
Learning and Inference in Computational Systems Biology, MIT Press:
Gaussian Processes for Missing Species in Biochemical Systems
Learning and Inference in Computational Systems Biology, MIT Press:
A Brief Introduction to Bayesian Inference
Learning and Inference in Computational Systems Biology, MIT Press:
Technical Reports
A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction
:
2009
Journal Papers
puma: a Bioconductor package for Propagating Uncertainty in Microarray Analysis
BMC Bioinformatics, 10(211):
Conference Papers
Non-Linear Matrix Factorization with Gaussian Processes
Proceedings of the International Conference in Machine Learning, Morgan Kauffman 26:
Latent Force Models
Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics, PMLR 5:9-16
Backing Off: Hierarchical Decomposition of Activity for 3D Novel Pose Recovery
British Machine Vision Conference, :
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
Advances in Neural Information Processing Systems, MIT Press 21:217-224
Technical Reports
Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes
:
Sparse Convolved Multiple Output Gaussian Processes
, :
2008
Journal Papers
Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities
Bioinformatics, 24:i70-i75
Conference Papers
Efficient Sampling for Gaussian Process Inference using Control Variables
Advances in Neural Information Processing Systems, MIT Press 21:1681-1688
Sparse Convolved Gaussian Processes for Multi-output Regression
Advances in Neural Information Processing Systems, MIT Press 21:57-64
Ambiguity Modeling in Latent Spaces
Machine Learning for Multimodal Interaction (MLMI 2008), Springer-Verlag:62-73
Topologically-Constrained Latent Variable Models
Proceedings of the International Conference in Machine Learning, Omnipress 25:1080-1087
Gaussian Process Latent Variable Models For Human Pose Estimation
Machine Learning for Multimodal Interaction (MLMI 2007), Springer-Verlag 4892:132-143
2007
Conference Papers
Model-driven detection of Clean Speech Patches in Noise
Proceedings of Interspeech 2007, :
Learning for Larger Datasets with the Gaussian Process Latent Variable Model
Proceedings of the Eleventh International Workshop on Artificial Intelligence and Statistics, Omnipress:243-250
Modelling transcriptional regulation using Gaussian Processes
Advances in Neural Information Processing Systems, MIT Press 19:785-792
Hierarchical Gaussian Process Latent Variable Models
Proceedings of the International Conference in Machine Learning, Omnipress 24:481-488
WiFi-SLAM Using Gaussian Process Latent Variable Models
Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), :2480-2485
Gaussian Process Latent Variable Models for Fault Detection
Computational Intelligence and Data Mining, :287-292
Miscellaneous
Variational Optimisation by Marginal Matching
:
2006
Journal Papers
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
Journal of Machine Learning Research, 7:455-491
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities
Bioinformatics, 22(22):2275-2281
Propagating Uncertainty in Microarray Data Analysis
Briefings in Bioinformatics, 7(1):37-47
Probe-level Measurement Error Improves Accuracy in Detecting Differential Gene Expression
Bioinformatics, 22(17):2107-2113
Conference Papers
Local Distance Preservation in the GP-LVM through Back Constraints
Proceedings of the International Conference in Machine Learning, Omnipress 23:513-520
Missing Data in Kernel PCA
ECML, Berlin, 2006, Springer-Verlag:751-758
Identifying submodules of cellular regulatory networks
International Conference on Computational Methods in Systems Biology, Springer-Verlag:
Fast Variational Inference for Gaussian Process Models through KL-Correction
ECML, Berlin, 2006, Springer-Verlag:270-281
Book Chapters
Gaussian Processes and the Null-Category Noise Model
Semi-supervised Learning, MIT Press:152-165
Technical Reports
Large Scale Learning with the Gaussian Process Latent Variable Model
(CS-06-05):
The Gaussian Process Latent Variable Model
(CS-06-03):
A Probabilistic Model to Integrate Chip and Microarray Data
(CS-06-02):
2005
Journal Papers
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
Journal of Machine Learning Research, 6:1783-1816
A Tractable Probabilistic Model for Affymetrix Probe-level Analysis across Multiple Chips
Bioinformatics, 21(18):3637-3644
Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis
Neurocomputing, 69:123-141
Accounting for Probe-level Noise in Principal Component Analysis of Microarray Data
Bionformatics, 21(19):3748-3754
Conference Papers
A Hybrid MaxEnt/HMM Based ASR System
Proceedings of Interspeech 2005 --- 9th European Conference on Speech Communication and Technology, :
Automatic Determination of the Number of Clusters Using Spectral Algorithms
Procedings of MLSP'05, :
Semi-supervised Learning via Gaussian Processes
Advances in Neural Information Processing Systems, MIT Press 17:753-760
Book Chapters
Extensions of the Informative Vector Machine
Deterministic and Statistical Methods in Machine Learning, Springer-Verlag 3635:56-87
Technical Reports
Variational Inference in Gaussian Processes via Probabilistic Point Assimilation
(CS-05-06):
Miscellaneous
MOCAP Toolbox for MATLAB
:
2004
Journal Papers
Reducing the Variability in cDNA Microarray Image Processing by Bayesian Inference
Bioinformatics, 20(4):518-526
Conference Papers
Learning to Learn with the Informative Vector Machine
Proceedings of the International Conference in Machine Learning, Omnipress 21:512-519
Gaussian Process Models for Visualisation of High Dimensional Data
Advances in Neural Information Processing Systems, MIT Press 16:329-336
Acoustic Space Dimensionality Selection and Combination using the Maximum Entropy Principle
International Conference on Acoustics, Speech and Signal Processing, :
Technical Reports
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
(CS-04-08):
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
(CS-04-13):
Matching Kernels through Kullback-Leibler Divergence Minimisation
(CS-04-12):
The Informative Vector Machine: A Practical Probabilistic Alternative to the Support Vector Machine
(CS-04-07):
2003
Journal Papers
A Probabilistic Model for the Extraction of Expression Levels from Oligonucleotide Arrays
Biochemical Transations, 31(6):1510-1512
Conference Papers
Bayesian Processing of Microarray Images
Neural Networks for Signal Processing XIII, IEEE:71-80
Variational Inference for Visual Tracking
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press I:773-780
A Variational Approach to Robust Bayesian Interpolation
Neural Networks for Signal Processing XIII, IEEE:229-238
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, :
Fast Sparse Gaussian Process Methods: The Informative Vector Machine
Advances in Neural Information Processing Systems, MIT Press 15:625-632
Technical Reports
Generalised Component Analysis
(CS-03-10):
2002
Conference Papers
Optimising Synchronisation Times for Mobile Devices
Advances in Neural Information Processing Systems, MIT Press 14:1401-1408
Technical Reports
Variational Inference Guide
:
Sparse Bayesian Learning: The Informative Vector Machine
:
2001
Journal Papers
A Comparison of State-of-the-Art Classification Techniques with Application to Cytogenetics
Neural Computing and Applications, 10(1):39-47
Conference Papers
Probabilistic Modelling of Replica Divergence
Proceedings of the 8th Workshop on Hot Topics in Operating Systems HOTOS (VIII), :
Node Relevance Determination
The {XI}th Italian Workshop on Neural Networks, Springer-Verlag:
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
Proceedings of the International Conference in Machine Learning, Morgan Kauffman 18:
Variational Learning for Multi-layer networks of Linear Threshold Units
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kauffman:245-252
Technical Reports
The Structure of Neural Network Posteriors
:
Miscellaneous
A Sparse Bayesian Compression Scheme — The Informative Vector Machine
:
2000
Technical Reports
Variational Learning for Multi-layer networks of Linear Threshold Units
:
Variational Bayesian Independent Component Analysis
:
1999
Technical Reports
A Variational Bayesian Committee of Neural Networks
:
1998
Conference Papers
Mixture Representations for Inference and Learning in Boltzmann Machines
Uncertainty in Artificial Intelligence, Morgan Kauffman 14:320-327
Approximating Posterior Distributions in Belief Networks using Mixtures
Advances in Neural Information Processing Systems, MIT Press 10:416-422
Technical Reports
Markovian inference in belief networks
:
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