Zobrazeno 1 - 10
of 1 137
pro vyhledávání: '"Bürkner, A"'
Autor:
Schmitt, Marvin, Li, Chengkun, Vehtari, Aki, Acerbi, Luigi, Bürkner, Paul-Christian, Radev, Stefan T.
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve both speed and accuracy when perform
Externí odkaz:
http://arxiv.org/abs/2409.04332
Autor:
Habermann, Daniel, Schmitt, Marvin, Kühmichel, Lars, Bulling, Andreas, Radev, Stefan T., Bürkner, Paul-Christian
Multilevel models (MLMs) are a central building block of the Bayesian workflow. They enable joint, interpretable modeling of data across hierarchical levels and provide a fully probabilistic quantification of uncertainty. Despite their well-recognize
Externí odkaz:
http://arxiv.org/abs/2408.13230
Generative models are a cornerstone of Bayesian data analysis, enabling predictive simulations and model validation. However, in practice, manually specified priors often lead to unreasonable simulation outcomes, a common obstacle for full Bayesian s
Externí odkaz:
http://arxiv.org/abs/2408.06504
Autor:
Magnusson, Måns, Torgander, Jakob, Bürkner, Paul-Christian, Zhang, Lu, Carpenter, Bob, Vehtari, Aki
The generality and robustness of inference algorithms is critical to the success of widely used probabilistic programming languages such as Stan, PyMC, Pyro, and Turing.jl. When designing a new general-purpose inference algorithm, whether it involves
Externí odkaz:
http://arxiv.org/abs/2407.04967
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation program (simulation-based inference; SBI). But how faithful is such infe
Externí odkaz:
http://arxiv.org/abs/2406.03154
Autor:
Fazio, Luna, Bürkner, Paul-Christian
Accounting for the complexity of psychological theories requires methods that can predict not only changes in the means of latent variables -- such as personality factors, creativity, or intelligence -- but also changes in their variances. Structural
Externí odkaz:
http://arxiv.org/abs/2404.14124
We develop a framework for derivative Gaussian process latent variable models (DGP-LVM) that can handle multi-dimensional output data using modified derivative covariance functions. The modifications account for complexities in the underlying data ge
Externí odkaz:
http://arxiv.org/abs/2404.04074
The simplex projection expands the capabilities of simplex plots (also known as ternary plots) to achieve a lossless visualization of 4D compositional data on a 2D canvas. Previously, this was only possible for 3D compositional data. We demonstrate h
Externí odkaz:
http://arxiv.org/abs/2403.11141
We present ActionDiffusion -- a novel diffusion model for procedure planning in instructional videos that is the first to take temporal inter-dependencies between actions into account in a diffusion model for procedure planning. This approach is in s
Externí odkaz:
http://arxiv.org/abs/2403.08591
Publikováno v:
Neural Information Processing Systems (NeurIPS 2024)
Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with this goal, we present consistency models for posterior estimation (CMPE
Externí odkaz:
http://arxiv.org/abs/2312.05440