### Browsing by Author "Lintusaari, Jarno"

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Item ELFI: Engine for likelihood-free inference(2018-08-01) Lintusaari, Jarno; Vuollekoski, Henri; Kangasrääsiö, Antti; Skytén, Kusti; Järvenpää, Marko; Marttinen, Pekka; Gutmann, Michael U.; Vehtari, Aki; Corander, Jukka; Kaski, Samuel; Department of Computer Science; Centre of Excellence in Computational Inference, COIN; Professorship Kaski Samuel; Helsinki Institute for Information Technology (HIIT); Probabilistic Machine Learning; Professorship Marttinen P.; Professorship Vehtari AkiEngine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for arranging components in LFI, such as priors, simulators, summaries or distances, to a network called ELFI graph. The components can be implemented in a wide variety of languages. The stand-alone ELFI graph can be used with any of the available inference methods without modifications. A central method implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference (BOLFI), which has recently been shown to accelerate likelihood-free inference up to several orders of magnitude by surrogate-modelling the distance. ELFI also has an inbuilt support for output data storing for reuse and analysis, and supports parallelization of computation from multiple cores up to a cluster environment. ELFI is designed to be extensible and provides interfaces for widening its functionality. This makes the adding of new inference methods to ELFI straightforward and automatically compatible with the inbuilt features.Item Fundamentals and Recent Developments in Approximate Bayesian Computation(2016) Lintusaari, Jarno; Gutmann, Michael; Dutta, Ritabrata; Kaski, Samuel; Corander, Jukka; Department of Computer Science; Centre of Excellence in Computational Inference, COIN; Professorship Kaski Samuel; Helsinki Institute for Information Technology (HIIT)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 problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) refers to a family of algorithms for approximate inference that makes a minimal set of assumptions by only requiring that sampling from a model is possible.We explain here the fundamentals of ABC, review the classical algorithms, and highlight recent developments.Item Resolving outbreak dynamics using approximate bayesian computation for stochastic birth–death models(Wellcome Trust, 2019) Lintusaari, Jarno; Blomstedt, Paul; Rose, Brittany; Sivula, Tuomas; Gutmann, Michael U.; Kaski, Samuel; Corander, Jukka; Department of Computer Science; Professorship Kaski Samuel; Probabilistic Machine Learning; Helsinki Institute for Information Technology (HIIT); Professorship Vehtari Aki; Finnish Center for Artificial Intelligence, FCAI; University of Helsinki; University of Edinburgh; University of OsloEarlier research has suggested that approximate Bayesian computation (ABC) makes it possible to fit simulator-based intractable birth–death models to investigate communicable disease outbreak dynamics with accuracy comparable to that of exact Bayesian methods. However, recent findings have indicated that key parameters, such as the reproductive number R, may remain poorly identifiable with these models. Here we show that this identifiability issue can be resolved by taking into account disease-specific characteristics of the transmission process in closer detail. Using tuberculosis (TB) in the San Francisco Bay area as a case study, we consider a model that generates genotype data from a mixture of three stochastic processes, each with its own distinct dynamics and clear epidemiological interpretation. We show that our model allows for accurate posterior inferences about outbreak dynamics from aggregated annual case data with genotype information. As a byproduct of the inference, the model provides an estimate of the infectious population size at the time the data were collected. The acquired estimate is approximately two orders of magnitude smaller than assumed in earlier related studies, and it is much better aligned with epidemiological knowledge about active TB prevalence. Similarly, the reproductive number R related to the primary underlying transmission process is estimated to be nearly three times larger than previous estimates, which has a substantial impact on the interpretation of the fitted outbreak model.Item Steps Forward in Approximate Computational Inference(Aalto University, 2019) Lintusaari, Jarno; Corander, Jukka, Prof., University of Oslo, Norway; Gutmann, Michael U., Dr., University of Edinburgh, UK; Tietotekniikan laitos; Department of Computer Science; Probabilistic Machine Learning; Perustieteiden korkeakoulu; School of Science; Kaski, Samuel, Prof., Aalto University, Department of Computer Science, FinlandThis thesis deals with approximate computational inference, particularly with a relatively recent approach in it known as approximate Bayesian computation (ABC). ABC deals with simulator-based models whose likelihood function is intractable. To overcome the intractability of the likelihood, ABC uses simulations from the model and a principled approximation of the posterior that is traditionally defined via a distance function and a threshold. I represent the ABC approximation as an approximation of the underlying likelihood function of the simulator-based model. This interpretation provides an intuitive way of understanding the approximation in ABC. I also consider the bias and Monte Carlo error in ABC, and demonstrate that better results can be acquired with a proper approximation than with a corresponding exact method in a given computational time. I further propose using an approximation of the likelihood function in investigating the reliability of ABC inferences. This approach reveals identifiability issues with a well-known disease transmission model for tuberculosis. A new transmission model is proposed that resolves these issues by more closely modelling the epidemiological process of tuberculosis. Updated estimates of the epidemiological parameters are then provided together with an estimate of the underlying infectious population that is better aligned with the epidemiological knowledge of the disease. Apart from ABC, I consider modelling computational inference problems with graphs, and how the graph representations can be used in the algorithmic level. The graph representations are used in learning Bayesian networks with more granular dependency structures. Finally, graphs are used for effective modelling of the ABC procedure and streamlining many aspects of the inference in a new open-source software called ELFI. In addition to graph-based modelling, ELFI provides distributed parallelization, data re-use and many other practical features for performing ABC inferences.