16 papers · ranked by Valyu relevance
Nathalie Paul, Venetia Karamitsou, Clemens Giegerich, Afshin Sadeghi + 4 more
'Moritz Lücke' 'Britta Wagenhuber' 'Alexander Kister' 'Markus Rehberg'] In the context of in silico clinical trials, mechanistic computer models for pathophysiology and pharmacology (here Quantitative Systems Pharmacology models, QSP) can greatly support the decision making for drug candidates and elucidate the…
Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski + 1 more
'Jukka Corander'] Title: Abstract Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference…
Niklas Smedemark-Margulies, Robin Walters, Heiko Zimmermann, Lucas Laird + 5 more
'Lucas Laird' 'Christian van der Loo' 'Neela Kaushik' 'Rajmonda Caceres' 'Jan-Willem van de Meent' 'Tom Britton'] Accurate epidemiological models require parameter estimates that account for mobility patterns and social network structure. We demonstrate the effectiveness of probabilistic programming for parameter…
Maya Horii, Aidan Gould, Zachary Yun, Jaideep Ray + 3 more
'Tarek Zohdi' 'Junhuan Zhang'] Accurate disease spread modeling is crucial for identifying the severity of outbreaks and planning effective mitigation efforts. To be reliable when applied to new outbreaks, model calibration techniques must be robust. However, current methods frequently forgo calibration verification (a…
Andreas Christ Sølvsten Jørgensen, Atiyo Ghosh, Marc Sturrock, Vahid Shahrezaei + 1 more
'Vahid Shahrezaei' 'Mark Alber'] The modelling of many real-world problems relies on computationally heavy simulations of randomly interacting individuals or agents. However, the values of the parameters that underlie the interactions between agents are typically poorly known, and hence they need to be inferred from…
Jan Kokko, Ulpu Remes, Owen Thomas, Henri Pesonen + 1 more
Likelihood-free inference for simulator-based models is an emerging methodological branch of statistics which has attracted considerable attention in applications across diverse fields such as population genetics, astronomy and economics. Recently, the power of statistical classifiers has been harnessed in…
Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach + 7 more
'Christian Weilbach' 'Vaden Masrani' 'William Harvey' 'Adam Ścibior' 'Boyan Beronov' 'John Grefenstette' 'Duncan Campbell' 'S. Ali Nasseri'] In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of…
Tom Burr, Alexei Skurikhin
Approximate Bayesian computation (ABC) is an approach for using measurement data to calibrate stochastic computer models, which are common in biology applications. ABC is becoming the “go-to” option when the data and/or parameter dimension is large because it relies on user-chosen summary statistics rather than the…
Timo M Deist, Andrew Patti, Zhaoqi Wang, David Krane + 3 more
We introduce simulation as a pre-processing step in a machine learning pipeline, in particular, as a way to include expert prior knowledge. One can consider simulation as a technique which regularizes data or as a specialized feature extraction method. In either view, the SimKern methodology offers a decomposition of…
Ronnie Henry
A Markov chain Monte Carlo (MCMC) simulation is a method of estimating an unknown probability distribution for the outcome of a complex process (a posterior distribution). Prior (capturing the concept prior to seeing any data) distributions are used to simulate sampling from variables that have known or closely…
Bas van Opheusden, Luigi Acerbi, Wei Ji Ma, Daniele Marinazzo
The fate of scientific hypotheses often relies on the ability of a computational model to explain the data, quantified in modern statistical approaches by the likelihood function. The log-likelihood is the key element for parameter estimation and model evaluation. However, the log-likelihood of complex models in fields…
Ingo Rohlfing
QCA has recently been subject to massive criticism and although the substance of that criticism is not completely new, it differs from earlier critiques by invoking simulations for the evaluation of QCA. In addition to debates about the meaning of the simulation results, there is a more fundamental discussion about…
Amani A. Alahmadi, Jennifer A. Flegg, Davis G. Cochrane, Christopher C. Drovandi + 1 more
'Christopher C. Drovandi' 'Jonathan M. Keith'] The behaviour of many processes in science and engineering can be accurately described by dynamical system models consisting of a set of ordinary differential equations (ODEs). Often these models have several unknown parameters that are difficult to estimate from…
Mostafa Herajy, Fei Liu, Christian Rohr, Monika Heiner
Background Hybrid simulation of (computational) biochemical reaction networks, which combines stochastic and deterministic dynamics, is an important direction to tackle future challenges due to complex and multi-scale models. Inherently hybrid computational models of biochemical networks entail two time scales: fast…
Janina Wellmann
Over the course of the last three decades, computer simulations have become a major tool of doing science and engaging with the world, not least in an effort to predict and intervene in a future to come. Born in the context of the Second World War and the discipline of physics, simulations have long spread into most…
Faraz Hussain, Christopher J Langmead, Qi Mi, Joyeeta Dutta-Moscato + 2 more
'Yoram Vodovotz' 'Sumit K Jha'] Background Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and…