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Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Univariate Discrete Distributions

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Introduction to the GiNaC Framework for Symbolic Computation within the C++ Programming Language

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Probabilistic LCF

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Reachability Analysis of Probabilistic Systems by Successive Refinements

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Model Repair Revamped

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Automatic Generation of Moment-Based Invariants for Prob-Solvable Loops

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Mora - Automatic Generation of Moment-Based Invariants

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Optimistic Value Iteration

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PrIC3: Property Directed Reachability for MDPs

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Analysis of Bayesian Networks via Prob-Solvable Loops

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Generating Functions for Probabilistic Programs

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Latticed k-Induction with an Application to Probabilistic Programs

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AQUA: Automated Quantized Inference for Probabilistic Programs

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Data-Driven Invariant Learning for Probabilistic Programs

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Does a Program Yield the Right Distribution?

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Probabilistic Program Verification via Inductive Synthesis of Inductive Invariants

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Expectation Invariants for Probabilistic Program Loops as Fixed Points

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Counterexample-Guided Polynomial Loop Invariant Generation by Lagrange Interpolation

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Probabilistic Inference by Program Transformation in Hakaru (System Description)

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Termination Analysis of Probabilistic Programs Through Positivstellensatz’s

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Synthesizing Probabilistic Invariants via Doob’s Decomposition

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PSI: Exact Symbolic Inference for Probabilistic Programs

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Bounded Model Checking for Probabilistic Programs

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Ensuring the Reliability of Your Model Checker: Interval Iteration for Markov Decision Processes

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Finding Polynomial Loop Invariants for Probabilistic Programs

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Fine-Grained Semantics for Probabilistic Programs

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Sound Value Iteration

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Proving Positive Almost-Sure Termination

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Linear-Invariant Generation for Probabilistic Programs:

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Probabilistic Program Analysis with Martingales

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Prinsys—On a Quest for Probabilistic Loop Invariants

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On the Positivity Problem for Simple Linear Recurrence Sequences,

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Abstraction, Refinement and Proof for Probabilistic Systems

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On the hardness of analyzing probabilistic programs

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About the Collatz conjecture

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Scenic: a language for scenario specification and data generation

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The computational complexity of probabilistic inference using bayesian belief networks

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On the hardness of approximate reasoning

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Semantics of probabilistic programs

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GiNaC—Symbolic computation with C++

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Foundations of Probabilistic Programming

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Semantics of Probabilistic Programming: A Gentle Introduction

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Termination Analysis of Probabilistic Programs with Martingales

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Concentration of Measure for the Analysis of Randomized Algorithms

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Analytic Combinatorics

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Probability and Computing

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The Monte Carlo Method

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Compositional Semantics for Probabilistic Programs with Exact Conditioning

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A type theory for probability density functions

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Probabilistic programming

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The Probabilistic Model Checking Landscape

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Verifying quantitative reliability for programs that execute on unreliable hardware

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Stochastic invariants for probabilistic termination

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Conditioning in Probabilistic Programming

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Weakest Precondition Reasoning for Expected Runtimes of Randomized Algorithms

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On the Computability of Conditional Probability

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Aiming low is harder: induction for lower bounds in probabilistic program verification

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λPSI: exact inference for higher-order probabilistic programs

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Scaling exact inference for discrete probabilistic programs

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Paradoxes of probabilistic programming: and how to condition on events of measure zero with infinitesimal probabilities

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Ranking and Repulsing Supermartingales for Reachability in Randomized Programs

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Central moment analysis for cost accumulators in probabilistic programs

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Quantitative analysis of assertion violations in probabilistic programs

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This is the moment for probabilistic loops

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Exact Recursive Probabilistic Programming

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Lower Bounds for Possibly Divergent Probabilistic Programs

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A = B

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A new approach to probabilistic programming inference

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R2: An Efficient MCMC Sampler for Probabilistic Programs

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The Computational Complexity of Probabilistic Planning

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Erweiterte wp-Kalküle für Probabilistische Programme

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Exact Probabilistic Inference Using Generating Functions

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Deriving Probability Density Functions from Probabilistic Functional Programs

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An Introduction to Probabilistic Programming

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A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs

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Does a Program Yield the Right Distribution? Verifying Probabilistic Programs via Generating Functions

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Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions

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Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions Artifact

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SymPy: symbolic computing in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Weighted Automata for Exact Inference in Discrete Probabilistic Programs

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Fixed-Point Reasoning for Stochastic Systems

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Generating Functions Meet Occupation Measures: Invariant Synthesis for Probabilistic Loops

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Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial Solving

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Parf: Adaptive Parameter Refining for Abstract Interpretation

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Guaranteed Bounds on Posterior Distributions of Discrete Probabilistic Programs with Loops

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Roulette: A Language for Expressive, Exact, and Efficient Discrete Probabilistic Programming

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Compiling with Generating Functions

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Guaranteed Inference for Probabilistic Programs: A Parallelisable, Small-Step Operational Approach

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Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies

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Parallelizable Feynman-Kac Models for Universal Probabilistic Programming

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