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检索条件"主题词=Probabilistic Programming"

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BayesLDM: A Domain-specific Modeling Language for probabilistic Modeling of Longitudinal Data 7

BayesLDM: A Domain-specific Modeling Language for Probabilis...

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7th IEEE/ACM Conference on Connected Health - Applications, Systems and Engineering Technologies (CHASE)

作者： Tung, Karine De La Torre, Steven El Mistiri, Mohamed De Braganca, Rebecca Braga Hekler, Eric Pavel, Misha Rivera, Daniel Klasnja, Pedja Spruijt-Metz, Donna Marlin, Benjamin M. Univ Massachusetts Amherst MA 01003 USA Univ Southern Calif Los Angeles CA USA Arizona State Univ Tempe AZ USA Univ Calif San Diego San Diego CA USA Northeastern Univ Boston MA USA Univ Michigan Ann Arbor MI USA

ISBN: (纸本)9781450394765

In this paper we present BayesLDM, a library for Bayesian longitudinal data modeling consisting of a high-level modeling language with specific features for modeling complex multivariate time series data coupled with a compiler that can produce optimized probabilistic program code for performing inference in the specified model. BayesLDM supports modeling of Bayesian network models with a specific focus on the efficient, declarative specification of dynamic Bayesian Networks (DBNs). The BayesLDM compiler combines a model specification with inspection of available data and outputs code for performing Bayesian inference for unknown model parameters while simultaneously handling missing data. These capabilities have the potential to significantly accelerate iterative modeling workflows in domains that involve the analysis of complex longitudinal data by abstracting away the process of producing computationally efficient probabilistic inference code. We describe the BayesLDM system components, evaluate the efficiency of representation and inference optimizations and provide an illustrative example of the application of the system to analyzing heterogeneous and partially observed mobile health data.

关键词： Bayesian inference probabilistic programming time series missing data Bayesian imputation mobile health

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Continualization of probabilistic Programs With Correction 1

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29th European Symposium on programming (ESOP) held as part of the European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Laurel, Jacob Misailovic, Sasa Univ Illinois Dept Comp Sci Urbana IL 61820 USA

ISBN: (数字)9783030449148

ISBN: (纸本)9783030449148;9783030449131

probabilistic programming offers a concise way to represent stochastic models and perform automated statistical inference. However, many real-world models have discrete or hybrid discrete-continuous distributions, for which existing tools may suffer non-trivial limitations. Inference and parameter estimation can be exceedingly slow for these models because many inference algorithms compute results faster (or exclusively) when the distributions being inferred are continuous. To address this discrepancy, this paper presents Leios. Leios is the first approach for systematically approximating arbitrary probabilistic programs that have discrete, or hybrid discrete-continuous random variables. The approximate programs have all their variables fully continualized. We show that once we have the fully continuous approximate program, we can perform inference and parameter estimation faster by exploiting the existing support that many languages offer for continuous distributions. Furthermore, we show that the estimates obtained when performing inference and parameter estimation on the continuous approximation are still comparably close to both the true parameter values and the estimates obtained when performing inference on the original model.

关键词： probabilistic programming Program Transformation Continuity Parameter Synthesis Program Approximation

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AcMC²: Accelerated Markov Chain Monte Carlo for probabilistic Models 24

AcMC<SUP>2</SUP>: Accelerated Markov Chain Monte Carlo for P...

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24th International Conference on Architectural Support for programming Languages and Operating Systems (ASPLOS)

作者： Banerjee, Subho S. Kalbarczyk, Zbigniew T. Iyer, Ravishankar K. Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781450362405

probabilistic models (PMs) are ubiquitously used across a variety of machine learning applications. They have been shown to successfully integrate structural prior information about data and effectively quantify uncertainty to enable the development of more powerful, interpretable, and efficient learning algorithms. This paper presents AcMC2, a compiler that transforms PMs into optimized hardware accelerators (for use in FPGAs or ASICs) that utilize Markov chain Monte Carlo methods to infer and query a distribution of posterior samples from the model. The compiler analyzes statistical dependencies in the PM to drive several optimizations to maximally exploit the parallelism and data locality available in the problem. We demonstrate the use of AcMC2 to implement several learning and inference tasks on a Xilinx Virtex-7 FPGA. AcMC2-generated accelerators provide a 47 - 100x improvement in runtime performance over a 6-core IBM Power8 CPU and a 8 - 18x improvement over an NVIDIA K80 GPU. This corresponds to a 753 - 1600x improvement over the CPU and 248 - 463x over the GPU in performance-per-watt terms.

关键词： Accelerator Markov Chain Monte Carlo probabilistic Graphical Models probabilistic programming

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Efficient Synthesis of probabilistic Programs 15

Efficient Synthesis of Probabilistic Programs

引用

36th ACM SIGPLAN Conference on programming Language Design and Implementation

作者： Nori, Aditya V. Ozair, Sherjil Rajamani, Sriram K. Vijaykeerthy, Deepak IIT Delhi Delhi India

ISBN: (纸本)9781450334686

We show how to automatically synthesize probabilistic programs from real-world datasets. Such a synthesis is feasible due to a combination of two techniques: (1) We borrow the idea of "sketching" from synthesis of deterministic programs, and allow the programmer to write a skeleton program with "holes". Sketches enable the programmer to communicate domain-specific intuition about the structure of the desired program and prune the search space, and (2) we design an efficient Markov Chain Monte Carlo (MCMC) based synthesis algorithm to instantiate the holes in the sketch with program fragments. Our algorithm efficiently synthesizes a probabilistic program that is most consistent with the data. A core difficulty in synthesizing probabilistic programs is computing the likelihood L (P vertical bar D) of a candidate program P generating data D. We propose an approximate method to compute likelihoods using mixtures of Gaussian distributions, thereby avoiding expensive computation of integrals. The use of such approximations enables us to speed up evaluation of the likelihood of candidate programs by a factor of 1000, and makes Markov Chain Monte Carlo based search feasible. We have implemented our algorithm in a tool called PSKETCH, and our results are encouraging-PSKETCH is able to automatically synthesize 16 non-trivial real-world probabilistic programs.

关键词： Design Experimentation Performance probabilistic programming Program Synthesis Markov Chain Monte Carlo

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Scalable Verification of probabilistic Networks 2019

Scalable Verification of Probabilistic Networks

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40th ACM SIGPLAN Conference on programming Language Design and Implementation (PLDI) part of ACM's Federated Computing Research Conference (FCRC)

作者： Smolka, Steffen Kumar, Praveen Kahn, David M. Foster, Nate Hsu, Justin Kozen, Dexter Silva, Alexandra Cornell Univ Ithaca NY 14850 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Wisconsin Madison WI USA UCL London England

ISBN: (纸本)9781450367127

This paper presents McNetKAT, a scalable tool for verifying probabilistic network programs. McNetKAT is based on a new semantics for the guarded and history-free fragment of probabilistic NetKAT in terms of finite-state, absorbing Markov chains. This view allows the semantics of all programs to be computed exactly, enabling construction of an automatic verification tool. Domain-specific optimizations and a parallelizing backend enable McNetKAT to analyze networks with thousands of nodes, automatically reasoning about general properties such as probabilistic program equivalence and refinement, as well as networking properties such as resilience to failures. We evaluate McNetKAT's scalability using real-world topologies, compare its performance against state-of-the-art tools, and develop an extended case study on a recently proposed data center network design.

关键词： Network verification probabilistic programming

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Survival prediction and risk estimation of Glioma patients using mRNA expressions 20

Survival prediction and risk estimation of Glioma patients u...

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20th IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Wijethilake, Navodini Meedeniya, Dulani Chitraranjan, Charith Perera, Indika Univ Moratuwa Dept Comp Sci Engn Moratuwa Sri Lanka

ISBN: (纸本)9781728195742

Gliomas are lethal type of central nervous system tumors with a poor prognosis. Recently, with the advancements in the micro-array technologies thousands of gene expression related data of glioma patients are acquired, leading for salient analysis in many aspects. Thus, genomics are been emerged into the field of prognosis analysis. In this work, we identify survival related 7 gene signature and explore two approaches for survival prediction and risk estimation. For survival prediction, we propose a novel probabilistic programming based approach, which outperforms the existing traditional machine learning algorithms. An average 4 fold accuracy of 74% is obtained with the proposed algorithm. Further, we construct a prognostic risk model for risk estimation of glioma patients. This model reflects the survival of glioma patients, with high risk for low survival patients.

关键词： Glioma gene expression risk score probabilistic programming bayesian neural networks

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Building Practical Statistical Relational Learning Systems

Building Practical Statistical Relational Learning Systems

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作者： Augustine, Eriq University of California Santa Cruz

学位级别：Ph.D., Doctor of Philosophy

In our increasingly connected world, data comes from many different sources, in many different forms, and is noisy, complex, and structured. To confront modern data, we need to embrace the structure inherent in the data and in the predictions. Statistical relational learning (SRL) is a subfield of machine learning that provides an effective means of approaching this problem of structured prediction. SRL frameworks use weighted logical and arithmetic expressions to easily create probabilistic graphical models (PGMs) to jointly reason over interdependent data. However, despite being well suited for modern, interconnected data, SRL faces several challenges that keep it from becoming practical and widely used in the machine learning community. In this dissertation, I address four pillars of practicality for SRL systems: scalability, expressivity, model adaptability, and usability. My work in this dissertation uses and extends probabilistic Soft Logic (PSL), a state-of-the-art open-source SRL framework. Scalability in SRL systems is essential for using large datasets and complex models. Because of the complex nature of interconnected data, models can easily outgrow available memory spaces. To address scalability for SRL, I developed methods that more efficiently and intelligently instantiate PGMs from templates and data. I also developed fixed-memory inference methods that can perform inference on very large models without requiring a proportional amount of memory. Expressivity allows SRL systems to represent many different problems and data patterns. Because SRL uses logical and arithmetic expressions to represent structured dependencies, SRL frameworks need to be able to express more than just what is represented by feature vectors. To address expressivity for SRL, I created a system to incorporate neural models with structured SRL inference, and expanded the interpretation of PSL weight hyperparameters to include additional types of distributions. Model adaptability i

关键词： Machine learning probabilistic graphical models probabilistic programming Scalability Statistical relational learning Usability probabilistic Soft Logic

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Optimization-based Decision-Making Support for Fuzzy and probabilistic Order Allocation Planning 8

Optimization-based Decision-Making Support for Fuzzy and Pro...

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8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Sutrisno Widowati Tjahjana, Redemtus Heru Diponegoro Univ Dept Math Semarang Indonesia

ISBN: (纸本)9786236264201;9786236264195

This paper proposed optimization-based decision-making support for solving the planning problems of raw material/product order allocation. A few parameters (prices, demand values, defective product rate, and late delivery) are uncertain and are treated as probabilistic or fuzzy depending on the data availability. Meanwhile, the parameters with historical/trial data are treated as probabilistic with some distribution functions. However, the parameters without any data are treated as fuzzy, and their corresponding membership functions are built by managers based on intuition and experience. Therefore, this study aims to determine optimal values for the decision variables, namely the number of raw materials planned to be ordered and its corresponding suppliers such that the total operational cost is expected to be minimal. These optimal decisions are calculated from the proposed optimization model in LINGO software by implementing the generalized Gradient algorithm. To evaluate and illustrate the proposed decision-making support, a numerical simulation was demonstrated. The results showed the optimal decisions were successfully attained and the expected minimal total operational cost was achieved. Furthermore, it proved that the proposed decision-making support could be implemented in manufacturing or retail industries to solve their order allocation problems.

关键词： decision-making support fuzzy programming order allocation problem probabilistic programming uncertain optimization

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PγωNK: Functional probabilistic NetKAT

PγωNK: Functional probabilistic NetKAT

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Proceedings of the ACM on programming Languages

作者： Vandenbroucke, Alexander Schrijvers, Tom KU Leuven Celestijnenlaan 200 A Leuven3001 Belgium

This work presents PγωNK, a functional probabilistic network programming language that extends probabilistic NetKAT (PNK). Like PNK, it enables probabilistic modelling of network behaviour, by providing probabilistic choice and infinite iteration (to simulate looping network packets). Yet, unlike PNK, it also offers abstraction and higher-order functions to make programming much more convenient. The formalisation of PγωNK is challenging for two reasons: Firstly, network programming induces multiple side effects (in particular, parallelism and probabilistic choice) which need to be carefully controlled in a functional setting. Our system uses an explicit syntax for thunks and sequencing which makes the interplay of these effects explicit. Secondly, measure theory, the standard domain for formalisations of (continuous) probablistic languages, does not admit higher-order functions. We address this by leveraging ω-Quasi Borel Spaces (ωQBSes), a recent advancement in the domain theory of probabilistic programming languages. We believe that our work is not only useful for bringing abstraction to PNK, but that-as part of our contribution-we have developed the meta-theory for a probabilistic language that combines advanced features like higher-order functions, iteration and parallelism, which may inform similar meta-theoretic efforts. © 2020 Copyright held by the owner/author(s).

关键词： Functional programming Abstracting Computer programming languages Higher order functions NetKAT Network modelling probabilistic language probabilistic modelling probabilistic programming probabilistic programming language Quasi Borel Spaces

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Effect Handlers for Programmable Inference 16

Effect Handlers for Programmable Inference

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16th ACM SIGPLAN International Symposium on Haskell (Haskell)

作者： Minh Nguyen Perera, Roly Wang, Meng Ramsay, Steven Univ Bristol Bristol Avon England

ISBN: (纸本)9798400702983

Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming. Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date. This paper presents an alternative approach to inference programming based on algebraic effects. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop two abstract algorithms, or inference patterns, representing two important classes of inference: Metropolis-Hastings and particle filtering. We show how our approach reveals the algorithms' high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the two inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-a-vis monad transformers as a structuring mechanism for modular imperative algorithm design.

关键词： probabilistic programming algebraic effects functional programming modularity

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