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Search · four archives
27 papers · ranked by Valyu relevance
Mikael Sunnåker, Alberto Giovanni Busetto, Elina Numminen, Jukka Corander + 3 more
Approximate Bayesian computation (ABC) constitutes a class of [computational methods]() rooted in [Bayesian statistics](). In all model-based [statistical inference](), the [likelihood function]() is of central importance, since it expresses the probability of the observed data under a particular statistical model, and…
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…
Wentao Li, Paul Fearnhead
Many statistical applications involve models for which it is difficult to evaluate the likelihood, but from which it is relatively easy to sample. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such cases. We present results on the asymptotic variance of estimators…
Ye Zheng, Stéphane Aris-Brosou
Studies on Approximate Bayesian Computation (ABC) replacing the intractable likelihood function in evaluation of the posterior distribution have been developed for several years. However, their field of application has to date essentially been limited to inference in population genetics. Here, we propose to extend this…
Gael M. Martin, David T. Frazier, Christian P. Robert
The 21st century has seen an enormous growth in the development and use of approximate Bayesian methods. Such methods produce computational solutions to certain 'intractable' statistical problems that challenge exact methods like Markov chain Monte Carlo: for instance, models with unavailable likelihoods…
Xiaoyu Wang, Adrianne L. Jenner, Robert Salomone, Christopher Drovandi
Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models can pose a challenge in terms of their ability to be validated with experimental measurements. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes…
Evgeny Tankhilevich, Jonathan Ish-Horowicz, Tara Hameed, Elisabeth Roesch + 3 more
Approximate Bayesian computation (ABC) is an important framework within which to infer the structure and parameters of a systems biology model. It is especially suitable for biological systems with stochastic and nonlinear dynamics, for which the likelihood functions are intractable. However, the associated…
Ulf Kai Mertens, Andreas Voss, Stefan Radev, Yong Deng
We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called ABrox is used to illustrate ABC for model comparison on two prominent statistical…
Wentao Li, Paul Fearnhead
We present asymptotic results for the regression-adjusted version of approximate Bayesian computation introduced by Beaumont et al. (2002). We show that for an appropriate choice of the bandwidth, regression adjustment will lead to a posterior that, asymptotically, correctly quantifies uncertainty. Furthermore, for…
Wilson González-Vanegas, Andrés Álvarez-Meza, José Hernández-Muriel, Álvaro Orozco-Gutiérrez
'Álvaro Orozco-Gutiérrez'] Bayesian statistical inference under unknown or hard to asses likelihood functions is a very challenging task. Currently, approximate Bayesian computation (ABC) techniques have emerged as a widely used set of likelihood-free methods. A vast number of ABC-based approaches have appeared in the…
Thomas Thorne, Paul D W Kirk, Heather A Harrington, Jonathan Wren
In Bayesian inference, we aim to derive the posterior distribution of the parameters of a model given some observed data. To do so we first define a prior distribution on the model parameters, treating them as random variables. This describes our belief in the distribution of the parameters before having observed any…
Anna Wawrzynczak, Piotr Kopka
Realistic modeling of complex physical phenomena is always quite a challenging task. The main problem usually concerns the uncertainties surrounding model input parameters, especially when not all information about a modeled phenomenon is known. In such cases, Approximate Bayesian Computation (ABC) methodology may be…
Grégoire Clarté, Christian P. Robert, Robin Ryder, Julien Stoehr
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty, we explore a Gibbs version of the…
Christopher Lester
We are in a position to set out the ML-ABC method. First, we settle on a choice of approximate sample resolutions, and therefore the set {Y_1_,…, Y _L_: where Y has time-step τ_ℓ_}. Then, With probability α_1_(ϕ_1_(Y_1_)) proceed to (using the aforementioned Poisson processes): Generate Y_2_ with time-step τ_2_. With…
Pierre Alquier
This is the Editorial article summarizing the scope of the Special Issue: Approximate Bayesian Inference.
Gael M. Martin, David T. Frazier, Christian P. Robert
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian analysis, with the rules of probability used to transform prior probability distributions for all unknowns — parameters, latent…
Max Hinne
Bayesian inference is becoming an increasingly popular framework for statistics in the behavioral sciences. However, its application is hampered by its computational intractability - almost all Bayesian analyses require a form of approximation. While some of these approximate inference algorithms, such as Markov chain…
Janet van Niekerk, Håvard Rue
Approximate inference methods like the Laplace method, Laplace approximations and variational methods, amongst others, are popular methods when exact inference is not feasible due to the complexity of the model or the abundance of data. In this paper we propose a hybrid approximate method namely Low-Rank Variational…
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
'Ulrike von Luxburg'] Full Bayesian posteriors are rarely analytically tractable, which is why real-world Bayesian inference heavily relies on approximate techniques. Approximations generally differ from the true posterior and require diagnostic tools to assess whether the inference can still be trusted. We investigate…
Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, Jakob H. Macke
Identifying parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference aims to not only find a single configuration of best-fitting parameters, but to recover all model parameters that are consistent with the data and prior knowledge.…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Lucian Chan, Geoffrey Hutchison, Garrett Morris
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
Qi Zhang, Chang Liu, Stephen Wu, Ryo Yoshida
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owing to the cost and technical difficulty of synthesizing such computationally designed molecules. To overcome such barriers, various methods…