Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 36 for “"Probabilistic Programming"”.
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Probabilistic Programming for Postoperative Bleeding
… of a tool from machine learning and statistics, probabilistic programming, into the clinical context, examining the assumptions inherent in these languages and comparing them with insights elicited from clinicians in context. We explore three research questions: what can we learn about clinical …
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Probabilistic data analysis with probabilistic programming
Probabilistic techniques are central to data analysis, but dierent approaches can be challenging to apply, combine, and compare. This thesis introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and …
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Computability, inference and modeling in probabilistic programming
… and thus ruling out the possibility of generic probabilistic inference algorithms (even inefficient ones), we highlight some positive results showing that posterior inference is possible in the presence of additional structure like exchangeability and noise, both of which are common in Bayesian …
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Scaling 3D Scene Perception via Probabilistic Programming
… robotics. In this thesis, we explore a probabilistic architecture for 3D perception based on structured generative models and probabilistic programs. We begin with 3DP3, the first iteration of our approach, which infers 3D scene graphs from real-world depth image data. 3DP3 demonstrates …
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Probabilistic Programming over Heterogeneous Language and Hardware Targets
Modern probabilistic programming applications, from large-scale Bayesian inference to real-time decision making, require both the expressiveness of CPU-oriented languages such as Gen.jl and the massive parallelism of GPU-backed array languages such as GenJAX, yet existing platforms force users to …
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Scaling Cooperative Intelligence via Inverse Planning and Probabilistic Programming
… building such systems via inverse planning and probabilistic programming. First, we introduce a probabilistic programming architecture that implements a Bayesian theory of mind. This architecture, Sequential Inverse Plan Search (SIPS), performs online inference of human goals and plans by …
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Serialization and Applications for the Gen Probabilistic Programming Language
Probabilistic programming has emerged as a powerful framework for building expressive models that can handle uncertainty in a wide range of applications. Serialization, the process of converting data structures or objects into a format suitable for storage or transmission, plays a crucial role in …
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Probabilistic Programming with Low-Level, High-Performance GPU Programmable Inference
GPU-compatible probabilistic programming languages (PPLs) have enabled high-performance, data-parallel programmable inference. However, these systems face fundamental trade-offs between expressiveness and performance, as their GPU code generation is automated and black-boxed, limiting optimization …
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PClean : Bayesian data cleaning at scale with domain-specific probabilistic programming
Data cleaning is naturally framed as probabilistic inference in a generative model, combining a prior distribution over ground-truth databases with a likelihood that models the noisy channel by which the data are filtered, corrupted, and joined to yield incomplete, dirty, and denormalized datasets. …
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Enhancing trustworthiness in probabilistic programming: systematic approaches for robust and accurate inference
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Bayesian optimization as a probabilistic meta-program
This thesis answers two questions: 1. How should probabilistic programming languages in- corporate Gaussian processes? and 2. Is it possible to write a probabilistic meta-program for Bayesian optimization, a probabilistic meta-algorithm that can combine regression frameworks such as Gaussian …
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Reduced traces and JITing in Church
Church is a Turing-complete probabilistic programming language, designed for inference. By allowing for easy description and manipulation of distributions, it allows one to describe classical Al models in compact ways, providing a language for very rich expression. However, for inference in Bayes …
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Automatic Integration and Differentiation of Probabilistic Programs
… distributions defined by higher-order probabilistic programs. It does this by developing a suite of composable program transformations for an expressive core calculus for probabilistic programming: • Integration: Compiling a probabilistic program into a deterministic representation of …
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Infrastructure for modeling and inference engineering with 3D generative scene graphs
Recent advances in probabilistic programming have enabled the development of probabilistic generative models for visual perception using a rich abstract representation of 3D scene geometry called a scene graph. However, there remain several challenges in the practical implementation of scene graph …
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Composable inference metaprogramming using subproblems
Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of hybrid probabilistic inference algorithms that apply different base probabilistic inference algorithms to different …
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Uncertainty-aware Joint Physical Tracking and Prediction
… embedded within a GPU-accelerated and parallel probabilistic programming system, maintains time-varying beliefs over both present and future object states, conditioned on observed images. These belief states are explicitly represented in symbolic form, enabling interpretable, frame-by-frame …
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Venture : an extensible platform for probabilistic meta-programming
… describes Venture, an extensible platform for probabilistic meta-programming. In Venture, probabilistic generative models, probability density functions, and probabilistic inference algorithms are all first-class objects. Any Venture program that makes random choices can be treated as a …
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Scalable Structure Learning, Inference, and Analysis with Probabilistic Programs
… and scale up the processes of learning accurate probabilistic models of complex data and obtaining principled solutions to probabilistic inference and analysis queries? This thesis presents efficient techniques for addressing these fundamental challenges grounded in probabilistic programming, …
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Formally justified and modular Bayesian inference for probabilistic programs
Probabilistic modelling offers a simple and coherent framework to describe the real world in the face of uncertainty. Furthermore, by applying Bayes' rule it is possible to use probabilistic models to make inferences about the state of the world from partial observations. While traditionally …
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