Subscribe free to our newsletters via your
. 24/7 Space News .




Subscribe free to our newsletters via your




















ROBO SPACE
ELFI: Engine for Likelihood-Free Inference facilitates more effective simulation
by Staff Writers
Espoo, Finland (SPX) Jan 06, 2017


This is an example of a simulator which models the spreading of an infectious disease. Image courtesy SysBio. For a larger version of this image please go here.

The Engine for Likelihood-Free Inference is open to everyone, and it can help significantly reduce the number of simulator runs.

Researchers have succeeded in building an engine for likelihood-free inference, which can be used to model reality as accurately as possible in a simulator. The engine may revolutionise the many fields in which computational simulation is utilised.

This development work is resulting in the creation of ELFI, an engine for likelihood-free inference, which will significantly reduce the number of exhausting simulation runs necessary for the estimation of unknown parameters and to which it will be easy to add new inference methods.

"Computational research is based in large part on simulation, and fitting simulator parameters to data is of key importance, in order for the simulator to describe reality as accurately as possible.

"The ELFI inference software we have developed makes this previously extremely difficult task as easy as possible: software developers can spread their new inference methods to widespread use, with minimal effort, and researchers from other fields can utilise the newest and most effective methods. Open software advances replicability and open science," says Samuel Kaski, professor at the Department of Computer Sciences and head of the Finnish Centre of Excellence in Computational Inference Research (COIN).

Software that is openly available to everyone is based on likelihood-free Bayesian inference, which is regarded as one of the most important innovations in statistics in the past decades.

The simulator's output is compared to actual observations, and due to their random -nature simulation runs must be carried out multiple times. The inference software will improve estimation of unknown parameters with e.g. Bayesian optimisation, which will significantly reduce the number of necessary simulation runs.

Applications from medicine to environmental science
ELFI users will likely be researchers from fields in which traditionally used statistical methods cannot be applied.

"Simulators can be applied in many fields. For example, a simulation of a disease can take into account how the disease is transmitted to another person, how long it will take for a person to recuperate or not recuperate, how a virus mutates or how many unique virus mutations exist. A number of simulation runs will therefore produce a realistic distribution describing the actual situation," Professor Aki Vehtari explains.

The ELFI inference engine is easy to use and scalable, and the inference problem can be easily defined with a graphical model.

"Environmental sciences and applied ecology utilise simulators to study the impact of human activities on the environment. For example, the Finnish Environment Institute (SYKE) is developing an ecosystem model, which will be used for the research of nutrient cycles in the Archipelago Sea and e.g. the impacts of loading caused by agriculture and fisheries to algal blooming.

"The parametrisation of these models and the assessment of the uncertainties related to their predictions is challenging from a computational standpoint. We will test the ELFI inference engine in these analyses. We hope that parametrisation of the models can be sped up and improved with ELFI, meaning that conclusions are better reasoned," says Assistant Professor Jarno Vanhatalo about environmental statistics research at the University of Helsinki.

Research paper


Comment on this article using your Disqus, Facebook, Google or Twitter login.

.


Related Links
Aalto University
All about the robots on Earth and beyond!






Share this article via these popular social media networks
del.icio.usdel.icio.us DiggDigg RedditReddit GoogleGoogle

Previous Report
ROBO SPACE
Fractional disturbance observers could help machines stay on track
Beijing, China (SPX) Jan 03, 2017
Roads are paved with obstacles than can interfere with our driving. They can be as easy to avoid or adjust to as far-away debris or as hard to anticipate as strong gusts of wind. As self-driving cars and other autonomous vehicles become a reality, how can researchers make sure these systems remain in control under highly uncertain conditions? A team of automation experts may have found a w ... read more


ROBO SPACE
Tech show looks beyond 'smart,' to new 'realities'

'Passengers' and the real-life science of deep space travel

NASA Readies for Major Orion Milestones in 2017

India achieves advances multiple space systems in 2016

ROBO SPACE
Europe and Russia looking at Space Tug Project

Preparing to Plug Into NASA SLS Fuel Tank

New round of wind tunnel tests underway for bigger SLS version

United Launch Alliance launches EchoStar XIX satellite

ROBO SPACE
Odyssey recovering from precautionary pause in activity

Small Troughs Growing on Mars May Become 'Spiders'

All eyes on Trump over Mars

Opportunity performs several drives to ancient gully

ROBO SPACE
China Plans to Launch 1st Mars Probe by 2020 - State Council Information Office

China to expand int'l cooperation on space sciences

China sees rapid development of space science and technology

Chinese missile giant seeks 20% of a satellite market

ROBO SPACE
Airbus DS and Energia eye new medium-class satellite platform

OneWeb announces key funding form SoftBank Group and other investors

Space as a Driver for Socio-Economic Sustainable Development

SoftBank delivers first $1 bn of Trump pledge, to space firm

ROBO SPACE
Rice U probes ways to turn cement's weakness to strength

Au naturel catalyst mimics nature to break tenacious carbon-hydrogen bond

Scientists create tiny laser using silver nanoparticles

Divide and conquer pattern searching

ROBO SPACE
The blob can learn and teach

Searching a sea of 'noise' to find exoplanets - using only data as a guide

Microlensing Study Suggests Most Common Outer Planets Likely Neptune-mass

Exciting new creatures discovered on ocean floor

ROBO SPACE
Exploring Pluto and the Wild Back Yonder

Juno Captures Jupiter 'Pearl'

Juno Mission Prepares for December 11 Jupiter Flyby

Research Offers Clues About the Timing of Jupiter's Formation




Memory Foam Mattress Review
Newsletters :: SpaceDaily :: SpaceWar :: TerraDaily :: Energy Daily
XML Feeds :: Space News :: Earth News :: War News :: Solar Energy News








The content herein, unless otherwise known to be public domain, are Copyright 1995-2017 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. Privacy Statement