AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms
Stan's Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm. It is challenging to implement these adaption schemes robustly in practice, hindering wider adoption amongst practitioners who are not directly working with the Stan modelling language. AdvancedHMC.jl (AHMC) contributes a modular, well-tested, standalone implementation of NUTS that recovers and extends Stan's NUTS algorithm. AHMC is written in Julia, a modern high-level language for scientific computing, benefiting from optional hardware acceleration and interoperability with a wealth of existing software written in both Julia and other languages, such as Python. Efficacy is demonstrated empirically by comparison with Stan through a third-party Markov chain Monte Carlo benchmarking suite.
Code (1)
Tasks
Bayesian InferenceBenchmarkingSimilar Papers 제목 키워드 기반
Modular approach to data preprocessing in ALOHA and application to a smart industry use case
Applications in the smart industry domain, such as interaction with collaborative robots using vocal commands or machine vision systems often requires the deployment of deep learning algorithms on heterogeneous low power…
Keyword SpottingMemEngine: A Unified and Modular Library for Developing Advanced Memory of LLM-based Agents
Recently, large language model based (LLM-based) agents have been widely applied across various fields. As a critical part, their memory capabilities have captured significant interest from both industrial and academic c…
Language ModelingLanguage ModellingLarge Language ModelFlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
With the advent of large language models (LLMs) and multimodal large language models (MLLMs), the potential of retrieval-augmented generation (RAG) has attracted considerable research attention. Various novel algorithms …
RAGRetrievalRetrieval-augmented GenerationBenchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical perform…
BenchmarkingEvolutionary AlgorithmsPobogot -- An Open-Hardware Open-Source Low Cost Robot for Swarm Robotics
This paper describes the Pogobot, an open-source and open-hardware platform specifically designed for research involving swarm robotics. Pogobot features vibration-based locomotion, infrared communication, and an array o…