toplogo
Resources
Sign In

Greedy Poisson Rejection Sampling: An Efficient Algorithm for One-Shot Channel Simulation


Core Concepts
Greedy Poisson rejection sampling (GPRS) is a new rejection sampling algorithm that can efficiently simulate samples from a target distribution Q using a proposal distribution P. GPRS achieves optimal average runtime and codelength for one-dimensional channel simulation problems where the target-proposal density ratio is unimodal.
Abstract
The paper introduces a new rejection sampling algorithm called greedy Poisson rejection sampling (GPRS). GPRS is designed to efficiently simulate samples from a target distribution Q using a proposal distribution P, with a focus on one-shot channel simulation problems. The key ideas behind GPRS are: Formulating the rejection sampling procedure as a search over the points of a Poisson process Π. GPRS accepts the first arrival of Π that falls under the graph of a carefully constructed function φ. Deriving an optimal stretch function σ to define φ = σ ◦r, where r = dQ/dP is the target-proposal density ratio. This ensures that the first arrival of Π under φ follows the target distribution Q. Proposing three variants of GPRS: A sequential search algorithm (Algorithm 3) A parallelized version that utilizes multiple threads (Algorithm 4) A branch-and-bound algorithm for one-dimensional problems where r is unimodal (Algorithm 5) Showing that each GPRS variant induces an optimal one-shot channel simulation protocol, where the average codelength is bounded by I[x; y] + log2(I[x; y] + 1) + O(1) bits. Proving that the branch-and-bound variant of GPRS achieves O(I[x; y]) runtime for one-dimensional problems with unimodal density ratios, which is optimal. Empirically demonstrating that GPRS outperforms the current state-of-the-art method, A* coding, on one-dimensional channel simulation problems.
Stats
The paper does not contain any explicit numerical data or statistics to support the key claims. The analysis is primarily theoretical, focusing on the correctness, runtime, and codelength guarantees of the proposed GPRS algorithms.
Quotes
"GPRS searches for the first arrival of a Poisson process Π under the graph of an appropriately defined function φ." "When x is a R-valued random variable and the density ratio dPx|y/dPx is always unimodal, the channel simulation protocol based on the binary search variant of GPRS achieves O(I[x; y]) runtime, which is optimal."

Key Insights Distilled From

by Gergely Flam... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2305.15313.pdf
Greedy Poisson Rejection Sampling

Deeper Inquiries

How can the GPRS framework be extended to handle multivariate target and proposal distributions, beyond the one-dimensional case considered in the paper

To extend the GPRS framework to handle multivariate target and proposal distributions, we need to consider the challenges and complexities that arise when moving beyond the one-dimensional case. One approach could involve adapting the rejection sampling algorithm to work in higher dimensions. This would require defining a suitable stretch function and acceptance criterion that can handle the increased complexity of multivariate distributions. One possible extension could involve using a multivariate Poisson process to simulate samples from the joint distribution of the target and proposal distributions. By considering the joint distribution in a higher-dimensional space, we can search for the first arrival of the Poisson process under a multivariate stretch function that captures the relationship between the target and proposal densities in multiple dimensions. This would involve defining a suitable metric or criterion for acceptance based on the multivariate density ratio. Additionally, incorporating techniques from multivariate analysis, such as copulas or multivariate probability distributions, could help in capturing the dependencies and interactions between variables in the target and proposal distributions. By leveraging these tools, we can design a more robust and efficient framework for handling multivariate distributions in the GPRS algorithm.

Can the lower bound on the average number of simulated samples be tightened to match the expected runtime of GPRS and A* sampling, or is there an algorithm that can achieve a lower runtime

Tightening the lower bound on the average number of simulated samples to match the expected runtime of GPRS and A* sampling would be a significant theoretical challenge. It would require a deep understanding of the fundamental principles underlying the sampling algorithms and the inherent complexities of the channel simulation problem. One potential direction for achieving a lower runtime could involve exploring advanced optimization techniques or algorithmic improvements that exploit the specific characteristics of the target and proposal distributions. By carefully designing the acceptance criteria, search strategies, or sampling procedures, it may be possible to develop an algorithm that outperforms the current lower bound on the average number of simulated samples. Alternatively, researchers could investigate novel sampling strategies, such as adaptive sampling or dynamic search algorithms, that adapt to the distribution characteristics in real-time. By incorporating adaptive elements into the sampling process, it may be possible to achieve a lower runtime while maintaining the accuracy and efficiency of the channel simulation protocol.

What other applications beyond one-shot channel simulation could benefit from the Poisson process-based sampling approach introduced in this work

The Poisson process-based sampling approach introduced in this work has broad applications beyond one-shot channel simulation. Some potential areas where this framework could be beneficial include: Statistical Modeling: The Poisson process can be used in statistical modeling and inference tasks, such as estimating parameters of complex distributions, generating synthetic data for simulation studies, or conducting hypothesis testing. The rejection sampling algorithm based on Poisson processes can provide a flexible and efficient way to sample from complex distributions. Machine Learning: In machine learning applications, the Poisson process-based sampling approach can be utilized for tasks such as generative modeling, data augmentation, or sampling from high-dimensional spaces. By incorporating the rejection sampling algorithm into machine learning models, researchers can improve the efficiency and accuracy of sampling procedures in various applications. Optimization: The Poisson process framework can also be applied in optimization problems, where sampling from probability distributions is a key component. By leveraging the rejection sampling algorithm based on Poisson processes, optimization algorithms can efficiently explore the solution space and make informed decisions based on sampled data points. Overall, the Poisson process-based sampling approach opens up a wide range of possibilities in diverse fields, offering a versatile and powerful tool for sampling from complex distributions and solving challenging computational problems.
0