Article
Economics
Filipe Rodrigues
Summary: This study proposes an amortized variational inference approach that utilizes stochastic backpropagation, automatic differentiation, and GPU-accelerated computation to enable Bayesian inference in mixed multinomial logit models on large datasets. Furthermore, it demonstrates how normalizing flows can enhance the flexibility of variational posterior approximations. Simulation and real data analysis show that this approach achieves significant computational speedups without compromising estimation accuracy on large datasets.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Environmental Sciences
Peter Reichert, Lorenz Ammann, Fabrizio Fenicia
Summary: Stochastic hydrological process models have two conceptual advantages over deterministic models: providing a more realistic description of the system and better accounting for structural deficits. However, stochastic models are more susceptible to identifiability problems and Bayesian inference is computationally more demanding.
WATER RESOURCES RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Yu Duan, Matthew D. Eaton, Michael J. Bluck
Summary: This paper introduces a novel online Bayesian calibration algorithm (FIPO-BC) that improves computational efficiency by using fixed inducing points and online learning capability. The algorithm is demonstrated in two test cases, showing its performance in finding optimal values and exploring posterior distributions of model parameters.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Economics
David Gunawan, Robert Kohn, David Nott
Summary: The study focuses on developing fast and accurate variational Bayes methods to approximate the posterior distribution of states and parameters in high dimensional multivariate factor stochastic volatility models, and extends it for prediction purposes; validated on simulated and real datasets, it shows to produce faster results compared to traditional approaches.
INTERNATIONAL JOURNAL OF FORECASTING
(2021)
Article
Environmental Sciences
Dayang Li, Lucy Marshall, Zhongmin Liang, Ashish Sharma, Yan Zhou
Summary: The study shows that by combining a residual error model based on deep learning with process-based hydrological models, better estimates of uncertainty in catchment modeling can be achieved. In the comparison of two catchments in China, the Bayesian LSTM method provides superior uncertainty estimates compared to the Bayesian linear regression model.
WATER RESOURCES RESEARCH
(2021)
Article
Biochemical Research Methods
Sebastian Persson, Niek Welkenhuysen, Sviatlana M. Shashkova, Samuel R. Wiqvist, Patrick M. Reith, Gregor R. Schmidt, Umberto M. Picchini, Marija R. Cvijovic
Summary: Understanding the causes and consequences of heterogeneity in cellular populations is crucial for disease treatment and population manipulation. In this study, we propose a Bayesian inference framework to elucidate sources of cell-to-cell variability in yeast signaling, providing deeper insights into the causes and consequences of heterogeneity.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Engineering, Civil
Marco Bacci, Marco Dal Molin, Fabrizio Fenicia, Peter Reichert, Jonas Sukys
Summary: This study tests the description of intrinsic uncertainty in conceptual models by using stochastic, time-dependent rate parameters. It analyzes the advantages and challenges of this approach and suggests its adoption and further development for providing a more realistic representation of uncertainties and diagnosing model deficiencies in future studies.
JOURNAL OF HYDROLOGY
(2022)
Article
Multidisciplinary Sciences
Gloria M. Monsalve-Bravo, Brodie A. J. Lawson, Christopher Drovandi, Kevin Burrage, Kevin S. Brown, Christopher M. Baker, Sarah A. Vollert, Kerrie Mengersen, Eve McDonald-Madden, Matthew P. Adams
Summary: This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. It identifies stiff parameter combinations affecting the model-data fit, and reveals which of these combinations are primarily influenced by the data or the priors. The technique is beneficial in contexts where data is limited compared to the number of model parameters, and has applications in biochemistry, ecology, and cardiac electrophysiology. It also helps uncover controlling mechanisms and guide parameter prioritization for improved parameter inference.
Article
Astronomy & Astrophysics
Marta Colleoni, Maite Mateu-Lucena, Hector Estelles, Cecilio Garcia-Quiros, David Keitel, Geraint Pratten, Antoni Ramos-Buades, Sascha Husa
Summary: In this study, the authors reanalyze the gravitational-wave event GW190412 using state-of-the-art phenomenological waveform models, focusing on the contribution from subdominant harmonics. They compare the PhenomX and PhenomT waveform models, discussing their construction techniques, computational efficiency, and agreement with other waveform models. Additionally, practical aspects of Bayesian inference, such as run convergence and computational cost, are also discussed.
Article
Physics, Fluids & Plasmas
Kai Shimagaki, John P. Barton
Summary: This article proposes a framework for accurately estimating time-integrated quantities using Bezier interpolation and applies it to two dynamical inference problems. The results show that Bezier interpolation reduces estimation bias, especially for data sets with limited time resolution.
Article
Economics
Suleyman Taspinar, Osman Dogan, Jiyoung Chae, Anil K. Bera
Summary: This study introduces a spatial stochastic volatility model with a Bayesian Markov chain Monte Carlo estimation method. Simulation results show satisfactory properties, and the practical usefulness is demonstrated through application on residential property price returns in the broader Chicago Metropolitan area.
OXFORD BULLETIN OF ECONOMICS AND STATISTICS
(2021)
Article
Multidisciplinary Sciences
Yuting Li, Guenther Turk, Paul B. Rohrbach, Patrick Pietzonka, Julian Kappler, Rajesh Singh, Jakub Dolezal, Timothy Ekeh, Lukas Kikuchi, Joseph D. Peterson, Austen Bolitho, Hideki Kobayashi, Michael E. Cates, R. Adhikari, Robert L. Jack
Summary: The study presents a Bayesian inference methodology for quantifying uncertainties in epidemiological forecasts, specifically for epidemics modeled by non-stationary, continuous-time, Markov population processes. The method's efficiency is derived from an approximation of the likelihood using a functional central limit theorem, which is valid for large populations. The methodology is demonstrated by analyzing the early stages of the COVID-19 pandemic in the UK, utilizing age-structured data for deaths.
ROYAL SOCIETY OPEN SCIENCE
(2021)
Article
Ecology
Johannes Oberpriller, David R. Cameron, Michael C. Dietze, Florian Hartig
Summary: Ecologists rely on complex computer simulations to forecast ecological systems, but uncertainties in model parameters and structure can lead to bias and underestimation. The article proposes a framework for robust inference and suggests solutions such as data rebalancing and bias corrections to improve model accuracy. Developing better methods for robust inference in complex computer simulations is crucial for generating reliable predictions of ecosystem responses.
Article
Mathematics
Francisco Javier Diez, Manuel Arias, Jorge Perez-Martin, Manuel Luque
Summary: OpenMarkov is an open-source software tool designed for probabilistic graphical models, primarily in medicine but also used in other fields and education in over 30 countries. This paper explains how OpenMarkov can be used as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, as well as various inference algorithms.
Article
Multidisciplinary Sciences
Kaan oecal, Michael U. Gutmann, Guido Sanguinetti, Ramon Grima
Summary: Estimating uncertainty in model predictions is a central task in quantitative biology. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. Synthetic models can substantially outperform state-of-the-art approaches, providing an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2022)
Article
Chemistry, Physical
Lorenzo Duso, Christoph Zechner
JOURNAL OF CHEMICAL PHYSICS
(2018)
Article
Developmental Biology
Dimitrios K. Papadopoulos, Kassiani Skouloudaki, Ylva Engstrom, Lars Terenius, Rudolf Rigler, Christoph Zechner, Vladana Vukojevic, Pavel Tomancak
Article
Multidisciplinary Sciences
A. Klosin, F. Oltsch, T. Harmon, A. Honigmann, F. Juelicher, A. A. Hyman, C. Zechner
Review
Developmental Biology
Christoph Zechner, Elisa Nerli, Caren Norden
Article
Multidisciplinary Sciences
Lorenzo Duso, Christoph Zechner
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2020)
Article
Biochemical Research Methods
Tobias Pietzsch, Lorenzo Duso, Christoph Zechner
Summary: This paper introduces a toolbox called Compartor that automatically generates moment equations for user-defined compartmentalized systems. Through the moment equation method, Compartor makes the analysis of stochastic population models more accessible and efficient for a broader scientific community.
Article
Biochemistry & Molecular Biology
Anders S. Hansen, Christoph Zechner
Summary: Cells respond to external signals and stresses by activating transcription factors, inducing gene expression changes. Different gene promoters exhibit distinct induction dynamics in response to the same TF input signal, suggesting that promoters can adopt context-dependent manifestations. The full complexity of signal processing by genetic circuits may be significantly underestimated when studied in only specific contexts.
MOLECULAR SYSTEMS BIOLOGY
(2021)
Article
Biochemical Research Methods
David T. Gonzales, Naresh Yandrapalli, Tom Robinson, Christoph Zechner, T-Y Dora Tang
Summary: The ability to build synthetic cellular populations from the bottom-up provides the groundwork to realize minimal living tissues. In this study, populations of monodisperse liposomes encapsulating cell-free expression systems were generated using microfluidics. The dynamics of transcription and translation within individual synthetic cells were quantified. Our experimental and theoretical approaches provide a statistically robust analysis of cell-free expression dynamics in bulk and monodisperse synthetic cell populations.
ACS SYNTHETIC BIOLOGY
(2022)
Article
Multidisciplinary Sciences
Michele Gabriele, Hugo B. Brandao, Simon Grosse-Holz, Asmita Jha, Gina M. Dailey, Claudia Cattoglio, Tsung-Han S. Hsieh, Leonid Mirny, Christoph Zechner, Anders S. Hansen
Summary: This study reveals the rare and dynamic nature of chromatin looping in the Fbn2 TAD, with a looped fraction of approximately 3 to 6.5% and a median loop lifetime of approximately 10 to 30 minutes. The results suggest that functional interactions may be primarily regulated by single CTCF boundaries rather than the fully CTCF-CTCF looped state.
Article
Biochemistry & Molecular Biology
Elisa Nerli, Jenny Kretzschmar, Tommaso Bianucci, Mauricio Rocha-Martins, Christoph Zechner, Caren Norden
Summary: Correct nervous system development requires the timely differentiation of progenitor cells into neurons. The fate decisions of neurogenic progenitors during development have been studied using live imaging in zebrafish retina. It was found that progenitor divisions produce one daughter cell with deterministic fate and one with probabilistic fate. Interfering with the deterministic branch affects lineage progression, while interfering with fate probabilities of the probabilistic branch results in a broader range of fate possibilities. A simple gene regulatory network can predict these fate decision probabilities during wild-type development. These findings highlight lineage flexibility in ensuring robust development of the retina and other tissues.
Article
Physics, Multidisciplinary
Anne-Lena Moor, Christoph Zechner
Summary: We develop numerical and analytical approaches to calculate mutual information between two molecular components embedded in a larger reaction network. We generalize our previous approach to biochemical networks involving multiple molecular components. We present an efficient Monte Carlo method and an analytical approximation to calculate the path mutual information and demonstrate its decomposition into transfer entropies. We apply our methodology to study information transfer in different network systems.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Physics, Multidisciplinary
Mohammadreza Bahadorian, Christoph Zechner, Carl D. Modes
PHYSICAL REVIEW RESEARCH
(2020)
Article
Biochemistry & Molecular Biology
David T. Gonzales, Christoph Zechner, T-Y Dora Tang
CURRENT OPINION IN SYSTEMS BIOLOGY
(2020)
Proceedings Paper
Automation & Control Systems
David T. Gonzales, T. -Y Dora Tang, Christoph Zechner
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC)
(2019)
Proceedings Paper
Automation & Control Systems
Lorenzo Duso, Christoph Zechner
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC)
(2019)
Article
Biochemical Research Methods
K. Ramki, G. Thiruppathi, Selva Kumar Ramasamy, P. Sundararaj, P. Sakthivel
Summary: A chromone-based ratiometric fluorescent probe L2 was developed for the selective detection of Hg(II) in a semiaqueous solution. The probe exhibited enhanced fluorescence in its aggregated state and even higher fluorescence when chelated with Hg(II). The probe demonstrated high sensitivity and specificity for Hg(II) detection and was successfully applied for imaging Hg(II) in a living model.
Article
Biochemical Research Methods
Qun Zhang, Rui Yang, Gang Liu, Shiyan Jiang, Jiarui Wang, Juqiang Lin, Tingyin Wang, Jing Wang, Zufang Huang
Summary: This research aims to develop a cost-effective and portable method for measuring creatinine levels using the enhanced Tyndall effect phenomenon. The method offers a promising solution for monitoring renal healthcare in resource-limited settings.
