23 papers · ranked by Valyu relevance
Lea Fritschi, Kerstin Lenk
Astrocytes are the largest subset of glial cells and perform structural, metabolic, and regulatory functions. They are directly involved in the communication at neuronal synapses and the maintenance of brain homeostasis. Several disorders, such as Alzheimer’s, epilepsy, and schizophrenia, have been associated with…
Janis Fluri, T. Kacprzak, Alexandre Réfrégier, Aurélien Lucchi + 1 more
'Thomas Hofmann'] The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this problem, we propose a novel approach to construct…
Y. Curtis Wang, Nirvik Sinha, Johann Rudi, James Velasco + 4 more
Experimental data-based parameter search for Hodgkin–Huxley-style (HH) neuron models is a major challenge for neuroscientists and neuroengineers. Current search strategies are often computationally expensive, are slow to converge, have difficulty handling nonlinearities or multimodalities in the objective function, or…
Michael J. Plank, Matthew J. Simpson
Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large.…
Matthew J. Simpson, Michael J. Plank
Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability, and model prediction are well-developed…
Julia Klein, Huy Phung, Matej Hajnal, David Šafránek + 2 more
'Tatjana Petrov' 'Ruriko Yoshida'] Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial…
Piotr Gwiazda, Alexey Kazarnikov, Anna Marciniak-Czochra, Zuzanna Szymańska
Calibrating mathematical models of biological processes is essential for achieving predictive accuracy and gaining mechanistic insight. However, this task remains challenging due to limited and noisy data, significant biological variability, and the computational complexity of the models themselves. In this method’s…
Matthew J. Simpson, Michael J. Plank
Parameter inference is a critical step in the process of interpreting biological data using mathematical models. Inference provides a means of deriving quantitative, mechanistic insights from sparse, noisy data. While methods for parameter inference, parameter identifiability and model prediction are well developed for…
Paul-Christian Bürkner, Marvin Schmitt, Stefan T. Radev
Simulations play important and diverse roles in statistical workflows, for example, in model specification, checking, validation, and even directly in model inference. Over the past decades, the application areas and overall potential of simulations in statistical workflows have expanded significantly, driven by the…
François Rousset, Raphäel Leblois, Arnaud Estoup, Jean-Michel Marin
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface…
Fuad S. Alduais, Neveen Sayed-Ahmed
The most essential process in statistical image and signal processing is the parameter estimation of probability density functions (PDFs). The estimation of the probability density functions is a contentious issue in the domains of artificial intelligence and machine learning. The study examines challenges related to…
François Rousset, Raphaël Leblois, Arnaud Estoup, Jean-Michel Marin
Simulation-based methods such as approximate Bayesian computation (ABC) are widely used to infer the evolutionary history of populations from molecular genetic data. We describe and evaluate a new iterative method of statistical inference about model parameters, which revisits the idea of inferring a likelihood surface…
Michael Evans, Siqi Zheng
Probability theory provides a clear definition of what is meant by evidence in favor, against or none either way, of an event occurring for an unobserved response, via the principle of evidence. This is immediately applicable when carrying out a proper Bayesian analysis. Even without a prior, this imposes restrictions…
Robert Reischke
Confidence contours in parameter space are a helpful tool to compare and classify determined estimators. For more intricate parameter estimations of non-linear nature or complex error structures, the procedure of determining confidence contours is a statistically complex task. For polymer chemists, such particular…
Russell J. Bowater
An overview is presented of a general theory of statistical inference that is referred to as the fiducial-Bayes fusion. This theory combines organic fiducial inference and Bayesian inference. The aim is that the reader is given a clear summary of the conceptual framework of the fiducial-Bayes fusion as well as pointers…
Klaus Mosegaard, Andrew Curtis
Bayesian inference is used to estimate continuous parameter values given measured data in many fields of science. The method relies on conditional probability densities to describe information about both data and parameters, yet the notion of conditional densities is inadmissible: probabilities of the same physical…
Michael Evans, Gun Ho Jang, Carlos Alberto De Bragança Pereira, Paulo Canas Rodrigues + 1 more
Relative belief inferences are shown to arise as Bayes rules or limiting Bayes rules. These inferences are invariant under reparameterizations and possess a number of optimal properties. In particular, relative belief inferences are based on a direct measure of statistical evidence.
Luai Al-Labadi, Michael Evans, Qiaoyu Liang, Ciprian Doru Giurcaneanu
'Ciprian Doru Giurcaneanu'] ROC (Receiver Operating Characteristic) analyses are considered under a variety of assumptions concerning the distributions of a measurement X in two populations. These include the binormal model as well as nonparametric models where little is assumed about the form of distributions. The…
Kevin Vanslette
This article expands the framework of Bayesian inference and provides direct probabilistic methods for approaching inference tasks that are typically handled with information theory. We treat Bayesian probability updating as a random process and uncover intrinsic quantitative features of joint probability distributions…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Jürgen Köfinger, Gerhard Hummer
The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we…
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
Owen Madin, Michael Shirts
Dispersion-repulsion interactions, commonly represented in atomistic force fields by the Lennard-Jones (LJ) potential, play an important role in the accuracy of molecular simulations. Training the force field parameters used in the LJ potential is challenging, generally requiring adjustment based on simulations of…