Uninet standalone

Uninet is a standalone uncertainty analysis software package. Its main focus is dependence modelling for high dimensional distributions. Random variables can be coupled using Bayesian networks, vine-copula constructions or dependence trees.

Read the Uninet help file describing the software in detail: UninetHelp.pdf (1.4 MB)

Visit the licensing page for details about the Uninet and UninetEngine licences and to find out how to acquire the latest versions.

UninetEngine library

Besides the Uninet application and its GUI, the functional core library UninetEngine can be used directly. The UninetEngine COM library is an extensive, object oriented, language-independent library containing over 70 classes with over 500 methods (functions).

The library can be used from a wide variety of programming languages: C++, C#, VB.net, Delphi, Matlab, R, Octave and VBA (as used by e.g. Excel) are a few languages in which frameworks using UninetEngine have been or are being written.

Several extra facilities are accessible through the programmatic interface, such as different samplers (e.g. the extremely fast pure memory sampler) and different ways of quantifying a Bayesnet (e.g. the arc correlations can be specified via a correlation matrix).

Uninet in publications

W Aspinall, RSJ Sparks, BE Hill, A Costa, C Connor, H Inakura, T Hasenaka, M Miyoshi, K Kiyosugi, T Tsuji, M Ushioda (2023) Aso volcano, Japan: assessing the 100-year probability of a new caldera-forming eruption based on expert judgements with Bayes Net and Importance Sampling uncertainty analysis, Journal of Applied Volcanology 2023 12:5. doi.org/10.1186/s13617-023-00131-8

X Li, Y Liu, R Abbassi, F Khan, R Zhang (2022) A Copula-Bayesian approach for risk assessment of decommissioning operation of aging subsea pipelines Process Safety and Environmental Protection, 167, 412-422, 2022 doi.org/10.1016/j.psep.2022.09.019

P D’Urso, L De Giovanni, V Vitale (2022) A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach Annals of Operations Research (2023) 325:419–440 doi.org/10.1007/s10479-022-04871-5

TM Gernon, TK Hincks, AS Merdith, EJ Rohling, MR Palmer, GL Foster, CP Bataille, RD Müller (2021) Global chemical weathering dominated by continental arcs since the mid-Palaeozoic Nature Geoscience, 2021 doi.org/10.1038/s41561-021-00806-0

WP Aspinall, RSJ Sparks, CB Connor, BE Hill, A Costa, JC Rougier, H Inakura, SH Mahony (2021) Aso volcano: estimating the probabilistic likelihood of a future Aso4-scale eruption from stochastic uncertainty analysis of volcanological evidence using importance sampling The 5th International Workshop on Rock Mechanics and Engineering Geology in Volcanic Fields (RMEGV2021: T Ito, T Ohta, M Osada, eds) ISRM2021 Specialized Conference September 9-11, 2021, Fukuoka, Japan; 6pp. ISBN 978-4-907430-05-4 doi.org/10.1201/9781003293590-11

RSJ Sparks, WP Aspinall, R Cooke, JH Scarrow (2020) Quantifying threat from COVID-19 infection hazard in Primary Schools in England MedRxiv, 2020 doi.org/10.1101/2020.08.07.20170035  Now in Royal Society Open Science, manuscript RSOS-202218

MJ Barons, W Aspinall (2020) Anticipated impacts of Brexit scenarios on UK food prices and implications for policies on poverty and health: a structured expert judgement approach, BMJ Open 2020; Volume 10, Issue 3. doi.org/10.1136/bmjopen-2019-032376

D Marella, P Vicard, V Vitale, D Ababei (2019) Measurement Error Correction by Nonparametric Bayesian Networks: Application and Evaluation, Statistical Learning of Complex Data, 155-162. doi.org/10.1007/978-3-030-21140-0_16

W Aspinall, G Woo (2019) Counterfactual Analysis of Runaway Volcanic Explosions Frontiers in Earth Science 7: 222. doi.org/10.3389/feart.2019.00222

JL Bamber, K Oppenheimer, RE Kopp, WP Aspinall, RM Cooke (2019) Ice sheet contributions to future sea level rise from structured expert judgement, PNAS first published May 20, 2019. doi.org/10.1073/pnas.1817205116  Uninet is mentioned in the Supplementary Information

W Aspinall, A Bevilacqua, A Costa, H Inakura, S Mahony, A Neri, R Sparks (2019) Probabilistic reconstruction (or forecasting) of distal runouts of large magnitude ignimbrite PDC flows sensitive to topography using mass‐dependent inversion models, Earth and Space Science Open Archive, 2020 doi.org/10.1002/essoar.10502300.1

RM Cooke, B Wielicki (2018) Probabilistic reasoning about measurements of equilibrium climate sensitivity: combining disparate lines of evidence, Climatic Change, 151(3), 541-554. doi.org/10.1007/s10584-018-2315-y

T Hincks, WA Aspinall, RM Cooke, T Gernon (2018) Oklahoma’s induced seismicity strongly linked to wastewater injection depth, Science, 1 Feb 2018. doi.org/10.1126/science.aap7911

AM Hanea, GF Nane, BA Wielicki, RM Cooke (2018) Bayesian networks for identifying incorrect probabilistic intuitions in a climate trend uncertainty quantification context, Risk Research pp 1-16, Published online: 26 Feb 2018.

BA Hradsky, TD Penman, D Ababei, A Hanea, EG Ritchie, A York, J Di Stefano (2017) Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions, Ecosphere 8(8). doi.org/10.1002/ecs2.1926

T Hincks, W Aspinall, S Sparks (2017) Application of Bayes Network analysis to RWGD siting: expert estimation of geological barrier effects due to climate change, Ch. 19 in: Geological Repository Systems for Safe Disposal of Spent Nuclear Fuels and Radioactive Waste (eds. Apted, M. & Ahn, J.), Elsevier, 551-581.  doi.org/10.1016/B978-0-08-100642-9.00019-0

C Liang, S Mahadevan (2016) Stochastic Multidisciplinary Analysis with High Dimensional Coupling AIAA Journal, Vol. 54, No. 4, pp. 1209-1219. arc.aiaa.org/doi/pdf/10.2514/1.J054343

C Liang, S Mahadevan (2016) Multidisciplinary optimization under uncertainty using Bayesian network, SAE International Journal of Materials and Manufacturing, Vol. 9, No. 2, pp. 419–429. saemobilus.sae.org/content/2016-01-0304/

C Liang (2016) Multidisciplinary Analysis and Optimization under Uncertainty, PhD Dissertation, Vanderbilt University, Department of Civil Engineering. etd.library.vanderbilt.edu/available/etd-02252016-204827/unrestricted/Liang.pdf

RM Cooke, TA Zang, DN Mavris, JC Tai (2015) Sculpting: A Fast, Interactive Method for Probabilistic Design Space Exploration and Margin Allocation, American Institute of Aeronautics and Astronautics. arc.aiaa.org/doi/abs/10.2514/6.2015-3440

C Van Gulijk, BJM Ale, D Ababei, M Steenhoek (2015) Comparison of risk profiles for chemical process plants using PLATYPUS, Safety and Reliability: Methodology and Applications, 2015, ISBN 978-1-138-02681-0, 1363-1368  Uninet provides the computational engine for Platypus

A Hanea, OM Napoles, D Ababei (2015) Non-parametric Bayesian networks: Improving theory and reviewing applications, Reliability Engineering & System Safety 144, 265-284. doi.org/10.1016/j.ress.2015.07.027

P Gradowska, RM Cooke (2013) Estimating expected value of information using Bayesian belief networks: a case study in fish consumption advisory, Environment, Systems and Decisions, March 2014, Volume 4, Issue 1, 88-97. doi.org/10.1007/s10669-013-9471-4

BJM Ale, C Van Gulijk, DM Hanea, P Hudson, PH Lin, S Sillem, et al (2013) Further development of a method to calculate frequencies of loss of control including their uncertainty, ESREL 2013: Proceedings of the 22nd European Safety and Reliability

AM Hanea, M Gheorghe, R Hanea, D Ababei (2013) Non-parametric Bayesian networks for parameter estimation in reservoir simulation: a graphical take on the ensemble Kalman filter (part I), Computational Geoscience 2013 17: 929. doi.org/10.1007/s10596-013-9365-z

AM Hanea, D Kurowicka, RM Cooke, D Ababei (2010) Mining and visualising ordinal data with non-parametric continuous BBNs, Computational Statistics & Data Analysis 54 (3), 668-687. doi.org/10.1016/j.csda.2008.09.032

BJM Ale, LJ Bellamy, J Cooper, D Ababei, D Kurowicka, O Morales, .et al (2010) Analysis of the crash of TK 1951 using CATS, Reliability Engineering & System Safety 95 (5), 469-477. doi.org/10.1016/j.ress.2009.11.014  The CATS model is implemented in Uninet

O Morales-Napoles, D Kurowicka, R Cooke, D Ababei (2007) Continuous-discrete distribution free Bayesian Belief Nets in aviation safety with Uninet, Technical Report TU Delft

RM Cooke, D Kurowicka, AM Hanea, O Morales Napoles, D Ababei, B Ale, A Roelen (2007) Continuous/Discrete Non Parametric Bayesian Belief Nets with Unicorn and Uninet, MMR 2007: Mathematical Methods in Reliability, Glasgow, UK, 1-4 July 2007

Uninet used by other software

FROST (Fire Regime Operations Simulation Tool)   New York Times
The Bayesian Networks in FROST (whose lead developer is Dan Ababei) are implemented using the UninetEngine library

Uninet screenshots

Model the meaning of life

Work with very large models – thousands of nodes

Random variables view – specify input random variables for your model and assign distributions

Bayesnet view – build your model with probabilistic nodes, functional nodes and arcs

Specify (conditional) rank correlation coefficients on the arcs, and formulas for functional nodes

Sample the model and view results

Perform analytical conditioning

Vine view – Sophisticated dependence modelling with regular vines

Explore multivariate joint distributions with Unigraph

Carry out sensitivity analysis with Unisens

And many more features to discover!