Markov chain Monte Carlo
PulseAugur coverage of Markov chain Monte Carlo — every cluster mentioning Markov chain Monte Carlo across labs, papers, and developer communities, ranked by signal.
- instance of Gotit.pub 90%
- developed CatalyzeX 70%
- used by Normalizing Flows 70%
- used by Metropolis-Hastings algorithm for extracting periodic gravitational wave signals from laser interferometric detector data 70%
- instance of Metropolis-Hastings algorithm for extracting periodic gravitational wave signals from laser interferometric detector data 70%
- competes with Variational Inference 70%
- instance of ScienceCast 60%
- used by ScienceCast 60%
- used by Gotit.pub 60%
- used by CatalyzeX 60%
- used by CORE Recommender 60%
- used by Influence Flower 60%
12 day(s) with sentiment data
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New method enables sparse Bayesian neural networks with exact zero-weight sampling
Researchers have developed a novel method for creating sparse Bayesian neural networks (BNNs) using sticky piecewise deterministic Markov processes (PDMPs). This approach directly incorporates pruning within a Bayesian …
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New Metropolis-Hastings generalization improves DAG sampling for Bayesian networks
Researchers have developed a new method to improve the efficiency of approximating posterior distributions for directed acyclic graphs (DAGs) in Bayesian network structure learning. This advancement introduces novel Mar…
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New Rondo testing method finds 117 bugs in deep learning compilers
Researchers have developed Rondo, a novel stateful testing method designed to uncover bugs in deep learning compilers. By representing test cases as model-invocation sequences, Rondo effectively identifies issues arisin…
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Conditional Mean Flows accelerate simulation-based inference with few-step density evaluation
Researchers have introduced Conditional Mean Flows, a novel approach to simulation-based inference that significantly speeds up density evaluation and posterior sampling. This method combines few-step sampling with trac…
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New research proves MCMC convergence diagnostics can't guarantee convergence
A new paper published on arXiv addresses the long-standing challenge of verifying convergence in Markov chain Monte Carlo (MCMC) methods. The research proves that no finite set of diagnostics can definitively guarantee …
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New Hierarchical Bayesian Method Enhances AI Uncertainty Quantification
Researchers have developed a new method called multilevel particle marginal Metropolis-Hastings (MLPMMH) to improve uncertainty quantification in AI by using Hierarchical Bayesian deep learning. This method combines pse…
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New generative models advance phylogenetic inference accuracy
Researchers have developed a new family of conditional generative models for phylogenetic tree inference, addressing limitations of conventional methods with complex evolutionary models. These models iteratively build t…
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REWIND method improves LLM reasoning by targeting error-prone generations
Researchers have developed REWIND, a novel training-free method designed to improve the reasoning capabilities of large language models. By analyzing token entropy, REWIND identifies and targets specific parts of a gene…
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New Bouncy Particle Sampler Enhances Sampling Efficiency for Complex Distributions
Researchers have developed a new method called Bouncy Particle Sampler with Parallel Tempering (BPS-PT) to improve sampling efficiency from complex probability distributions. This technique builds upon the Bouncy Partic…
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New Reweighted ALPS method improves multimodal distribution sampling
Researchers have developed a new method called Reweighted ALPS (Re-ALPS) to improve sampling from multimodal distributions, a common challenge in Bayesian inference and machine learning. This technique aims to overcome …
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New SCOPE Method Accelerates Bayesian Predictive Inference
Researchers have developed SCOPE (Score-guided Conformal Posterior Exploration), a novel method for Bayesian predictive inference that significantly reduces computational cost while maintaining calibration. SCOPE levera…
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New NFDiag method enhances MCMC convergence diagnosis in high dimensions
Researchers have introduced NFDiag, a novel method for diagnosing the convergence of Markov chain Monte Carlo (MCMC) simulations, particularly in high-dimensional scenarios. Unlike traditional diagnostics that focus on …
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New generative models offer more accurate phylogenetic tree inference
Researchers have developed a new family of conditional generative models for phylogenetic tree inference, addressing limitations of conventional methods that rely on simplified evolutionary models. These models, trained…
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New OSIS method offers efficient one-step amortised inference for Bayesian sampling
Researchers have developed a novel one-step implicit sampler (OSIS) designed to efficiently draw unbiased samples from Bayesian posterior distributions in high dimensions. This method leverages short-time Markov chain M…
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Diffusion models and MCMC accelerate spacecraft trajectory optimization
Researchers have developed a novel transfer-learning framework that enhances the efficiency of generating spacecraft trajectories for preliminary mission design. This approach combines diffusion models with Markov Chain…
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New method enhances MCMC sampling with learned variance reduction
Researchers have developed a new method for improving Markov chain Monte Carlo (MCMC) methods by using learned transformations to reduce variance in estimates. This approach involves training a bijection, such as a norm…
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New PreSTO method accelerates LLM power sampling, with critical analysis of its effectiveness · 3 sources tracked
Researchers have developed PreSTO, a new method to accelerate LLM power sampling by prefetching future proposals. This technique, detailed in an arXiv paper, views future sampling outcomes as a tree and generates propos…
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New MCMC sampler enhances Bayesian inference for switching stochastic differential equations
Researchers have developed a new approximate Markov chain Monte Carlo sampler for switching stochastic differential equations (SSDEs). This method utilizes uniformization and time-conditioned factorized neural likelihoo…
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New sparse graph method uses Hamiltonian dynamics for multimodal sampling
Researchers have developed a novel sparse graph method for transporting probability mass towards multimodal target distributions using damped nonlocal Hamiltonian dynamics. This approach combines logarithmic-mean mobili…
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New flow-based models accelerate solutions for complex inverse problems · 2 sources tracked
Two new research papers explore advanced methods for solving inverse problems using flow-based generative models. The first paper introduces Conditional Flow Matching as an amortized alternative to traditional Markov ch…