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Predicting abdominal aortic aneurysm growth using patient-oriented growth models with two-step Bayesian inference

COMPUTERS IN BIOLOGY AND MEDICINE 2020年第0期117卷 103620-000页

作者： Akkoyun, Emrah Kwon, Sebastian T. Acar, Aybar C. Lee, Whal Baek, Seungik Middle East Tech Univ Grad Sch Informat Dept Hlth Informat Dumlupinar Bulvari 1 TR-06800 Ankara Turkey Univ Calif Los Angeles David Geffen Sch Med Dept Anesthesiol & Perioperat Med 757 Westwood Blvd Los Angeles CA 90095 USA Seoul Natl Univ Hosp Dept Radiol 101 Daehangno Seoul South Korea Michigan State Univ Dept Mech Engn 2457 Engn Bldg E Lansing MI 48824 USA

Objective: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter. Methods: 106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler. Results: The follow-up diameters were predicted satisfactorily (i.e. the true follow-up diameter was in the 95% prediction interval) for 79% of the scans using the population-based growth model, and 83% of the scans using the patient-specific growth model. Among the evaluated geometric measurements, centerline tortuosity was a significant (p = 0.0002) predictor of growth for AAAs with accelerated and stable expansion rates. Using the enhanced prediction model, 86% of follow-up scans were predicted satisfactorily. The average prediction errors of population-based, patient-specific, and enhanced models were +/- 2.67, +/- 2.61 and +/- 2.79 mm, respectively. Conclusion: A computational framework using patient-oriented growth models provides useful tools for perpatient basis treatment and enables better prediction of AAA growth.

关键词： Abdominal aortic aneurysm probabilistic programming Bayesian inference Rupture risk assessment Patient-oriented prediction model

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Konvexita v úlohách s pravděpodobnostními omezeními

Konvexita v úlohách s pravděpodobnostními omezeními

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作者： Olos, Marek Charles University of Prague

This thesis deals with chance constrained stochastic programming pro- blems. The first chapter is an introduction. We formulate several stochastic pro- gramming problems in the second chapter. In chapter 3 we present the theory of α-concave functions and measures as a basic tool for proving convexity of the problems formulated in chapter 2 for the continuous distributions of the random vectors. We use the results of the theory to characterize a large class of the conti- nuous distributions, that satisfy the sufficient conditions for the convexity and to prove convexity of concrete sets. In chapter 4 we present sufficient conditions for the convexity of the problems and we briefly discuss the method of the p-level ef- ficient points. In chapter 5 we solve a portfolio selection problem using Kataokas model. 1

关键词： probabilistic programming chance constraint convexity

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Program equivalence in a typed probabilistic call-by-need functional language

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JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN programming 2023年 135卷

作者： Schmidt-Schauss, Manfred Sabel, David Goethe Univ Frankfurt Main Frankfurt Germany

We extend a call-by-need variant of PCF with a binary probabilistic fair choice operator, which makes a lazy and typed variant of probabilistic functional programming. We define a contextual equivalence that respects the expected convergence of expressions and prove a corresponding context lemma. This enables us to show correctness of several program transformations with respect to contextual equivalence. Distribution -equivalence of expressions of numeric type is introduced. While the notion of contextual equivalence stems from program semantics, the notion of distribution equivalence is a direct description of the stochastic model. Our main result is that both notions are compatible: We show that for closed expressions of numeric type contextual equivalence and distribution-equivalence coincide. This provides a strong and often operationally feasible criterion for contextual equivalence of expressions and programs.(c) 2023 Elsevier Inc. All rights reserved.

关键词： probabilistic programming Lambda-calculus Semantics Call-by-need evaluation Program transformations

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Direct Bayesian inference for fault severity assessment in Digital-Twin-Based fault diagnosis

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ANNALS OF NUCLEAR ENERGY 2023年第1期194卷

作者： Nguyen, Tat Nghia Vilim, Richard B. Argonne Natl Lab Nucl Sci & Engn Div Lemont IL 60439 USA

For applications in condition-based maintenance of nuclear systems, the assessment of fault severity is crucial. In this work, we developed a framework that allows for direct inference of the probability distributions of possible faults in a system. We employed a model-based approach with model residuals generated from analytical redundancy relations provided by physics-based models of the system components. From real-time sensor readings, the values of the model residuals can be calculated, and the posterior probability distributions of the faults can be computed directly using the methods of Bayesian networks. From the posterior distribution of each fault, one can estimate the fault probability based on a chosen threshold and assess the severity of the fault. By eliminating the discretization and simplifications in middle steps, this approach allows us to leverage the available computational resources to provide more accurate fault probability estimates and severity assessments.

关键词： Model-based diagnosis Fault detection Bayesian inference probabilistic programming Digital twin

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programming the World of Uncertain Things (Keynote)

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ACM SIGPLAN NOTICES 2016年第1期51卷 1-2页

作者： McKinley, Kathryn S. Microsoft Res Redmond WA USA

Computing has entered the era of uncertain data, in which hardware and software generate and reason about estimates. Applications use estimates from sensors, machine learning, big data, humans, and approximate hardware and software. Unfortunately, developers face pervasive correctness, programmability, and optimization problems due to estimates. Most programming languages unfortunately make these problems worse. We propose a new programming abstraction called Uncertain < T > embedded into languages, such as C#, C++, Java, Python, and JavaScript. Applications that consume estimates use familiar discrete operations for their estimates;overloaded conditional operators specify hypothesis tests and applications use them control false positives and negatives;and new compositional operators express domain knowledge. By carefully restricting the expressiveness, the runtime automatically implements correct statistical reasoning at conditionals, relieving developers of the need to implement or deeply understand statistics. We demonstrate substantial programmability, correctness, and efficiency benefits of this programming model for GPS sensor navigation, approximate computing, machine learning, and xBox.

关键词： Languages Verification Measurement programming with estimates probabilistic programming Bayesian logic Lazy evaluation

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Generative Datalog with Continuous Distributions

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JOURNAL OF THE ACM 2022年第6期69卷 46-46页

作者： Grohe, Martin Kaminski, Benjamin Lucien Katoen, Joost-Pieter Lindner, Peter Rhein Westfal TH Aachen Ahornstr 55 Aachen Germany Univ Saarland Saarland Informatics Campus Campus E1 3 Saarbrucken Germany UCL 66-72 Gower St London England

Arguing for the need to combine declarative and probabilistic programming, Barany et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a "purely declarative probabilistic programming language?' We revisit this language and propose a more principled approach towards defining its semantics based on stochastic kernels and Markov processes-standard notions from probability theory. This allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by Barany et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, LMCS 2022) and show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.

关键词： Datalog probabilistic databases Generative Datalog measure theory stochastic kernels probabilistic programming

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Uncertainty reduction and quantification in computational thermodynamics

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COMPUTATIONAL MATERIALS SCIENCE 2022年 212卷

作者： Otis, Richard CALTECH Jet Prop Lab Engn & Sci Directorate Pasadena CA 91109 USA

Uncertainty quantification is an important part of materials science and serves a role not only in assessing the accuracy of a given model, but also in the rational reduction of uncertainty via new models and experiments. In this review, recent advances and challenges related to uncertainty quantification for Calphad-based thermodynamic and kinetic models are discussed, with particular focus on approaches using Markov chain Monte Carlo sampling methods. General differentiable and probabilistic programming frameworks are identified as an enabling and rapidly-maturing new technology for scalable uncertainty quantification in complex physics-based models. Special challenges for uncertainty reduction and Bayesian design-of-experiment for improving Calphadbased models are discussed in light of recent progress in related fields.

关键词： Uncertainty quantification Optimal experimental design CALPHAD Markov chain Monte Carlo Differentiable programming probabilistic programming Automatic differentiation

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Control-data separation and logical condition propagation for efficient inference on probabilistic programs

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JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN programming 2024年 136卷

作者： Hasuo, Ichiro Oyabu, Yuichiro Eberhart, Clovis Suenaga, Kohei Cho, Kenta Katsumata, Shin-ya Natl Inst Informat Hitotsubashi 2-1-2Chiyoda Tokyo 1018430 Japan SOKENDAI Grad Univ Adv Studies Dept Informat Hayama Kanagawa 2400193 Japan Ernst & Young ShinNihon LLC Yurakucho 1-1-2Chiyoda Tokyo 1000006 Japan Kyoto Univ Grad Sch Informat Yoshida Honmachi 36-1 Kyoto Kyoto 6068501 Japan

We present a novel sampling framework for probabilistic programs. The framework combines two recent ideas-control-data separation and logical condition propagation-in a nontrivial manner so that the two ideas boost the benefits of each other. We implemented our algorithm on top of Anglican. The experimental results demonstrate our algorithm's efficiency, especially for programs with while loops and rare observations. (c) 2023 Elsevier Inc. All rights reserved.

关键词： probabilistic programming Bayesian inference Sampling Static analysis Program logic

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The Static Approach in Stochastic Limit Analysis for Reliability Structural Assessment

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Safety and Reliability 2012年第3期32卷 4-29页

The paper presents firstly a brief review of the basic principles of Stochastic Limit Analysis, with particular reference to the two theorems that allow to bound the probability of collapse of a structure when the acting forces are deterministic and/or random patterns. The two theorems give place to two conceptually different approaches, although they should produce in principle the same results. Since both procedures can be most times applied only for approximate evaluation of the collapse probability, the major points and the specific features of the two methods, the kinematical and the static method, are reasserted with the purpose to prevent possible misunderstanding due to their deterministic complementarity. Moreover, the problem of the convergence of the static approach to the true collapse probability is discussed; a problem that is not so hardly felt for the kinematical method, whose exploitation, at least for discrete structures with a finite number of possible collapse modes, is quite trivial.

关键词： Selective maintenance reliability chance constraint probabilistic programming

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Tree-AMP: compositional inference with tree approximate message passing

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 2408-2496页

作者： Antoine Baker Florent Krzakala Benjamin Aubin Lenka Zdeborová Laboratoire de Physique CNRS École Normale Supérieure PSL University Paris France Institut de Physique Théorique CNRS CEA Université Paris-Saclay Saclay France

We introduce Tree-AMP, standing for Tree Approximate Message Passing, a python package for compositional inference in high-dimensional tree-structured models. The package provides a unifying framework to study several approximate message passing algorithms previously derived for a variety of machine learning tasks such as generalized linear models, inference in multi-layer networks, matrix factorization, and reconstruction using nonseparable penalties. For some models, the asymptotic performance of the algorithm can be theoretically predicted by the state evolution, and the measurements entropy estimated by the free entropy formalism. The implementation is modular by design: each module, which implements a factor, can be composed at will with other modules to solve complex inference tasks. The user only needs to declare the factor graph of the model: the inference algorithm, state evolution and entropy estimation are fully automated. The source code is publicly available at https://***/sphinxteam/tramp and the documentation at https://***/***.

关键词： probabilistic programming graphical models Bethe entropy state evolution expectation propagation

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