A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Machine learning techniques improve Bayesian computation for complex data.
This Chapter, "Overview of Approximate Bayesian Computation", is to appear as the first chapter in the forthcoming Handbook of Approximate Bayesian Computation (2018). It details the main ideas and concepts behind ABC methods with many examples and illustrations.
QBC uses quantum computers to speed up Bayesian computation.
The paper proposes using path signatures for better inference in time series data.
This paper considers the computational power of constant size, dynamic Bayesian networks. Although discrete dynamic Bayesian networks are no more powerful than hidden Markov models, dynamic Bayesian networks with continuous random variables and discrete children of continuous parents are capable of performing Turing-co…
Bayesian methods enhance deep learning models by improving reliability and uncertainty.
Bayesian Deep Learning tackles inverse problems with neural networks and approximate computations.
Bayesian optimization has emerged as a strong candidate tool for global optimization of functions with expensive evaluation costs. However, due to the dynamic nature of research in Bayesian approaches, and the evolution of computing technology, using Bayesian optimization in a parallel computing environment remains a c…
New BNN architectures reduce computational cost for uncertainty quantification.
A new method for efficient computation of Knowledge Gradient in Bayesian optimization.
A new method improves robustness and efficiency of Bayesian LOO-CV.
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learni…
The classical approach to inverse problems is based on the optimization of a misfit function. Despite its computational appeal, such an approach suffers from many shortcomings, e.g., non-uniqueness of solutions, modeling prior knowledge, etc. The Bayesian formalism to inverse problems avoids most of the difficulties en…
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures. The former lead to low computational time but require the selection of a regularization parameter, whose value significantly impacts the achieved solution, and whos…
Bayesian design improves experimental optimization.
Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. In these applications, distributed and scalable architectures are a necessity. However, Bayesian optimization is mo…
New method improves ABC for Bayesian model comparison.
Gradient-EM Bayesian meta-learning accelerates adaptation with reduced computation and improved robustness.
A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time. In results of our experiment, we observe that the proposed method can reduce the computational time to less than one-tenth of …
Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been giv…
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate mode…
Paper proposes efficient method for evaluating Bayesian models in imaging.
This paper provides efficient algorithms for computing entropy and KL divergence in Bayesian networks.
We introduce new definitions of universal and superuniversal computable codes, which are based on a code's ability to approximate Kolmogorov complexity within the prescribed margin for all individual sequences from a given set. Such sets of sequences may be singled out almost surely with respect to certain probability …
A new framework for efficient Bayesian network inference.
Efficiently discovers Bayesian network structure with reduced memory usage.
Bayesian data sketching speeds up inference for large functional data.
This paper examines how neural architectures support amortized Bayesian inference and its performance under varying conditions.
Efficient algorithm for Bayesian networks reduces marginal probability distribution computation.
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
Develops a new Bayesian inference method for discrete data.
Bayesian neural networks (BNN) can estimate the uncertainty in predictions, as opposed to non-Bayesian neural networks (NNs). However, BNNs have been far less widely used than non-Bayesian NNs in practice since they need iterative NN executions to predict a result for one data, and it gives rise to prohibitive computat…
Stochastic Volatility in Mean models with heavy-tailed distributions using Hidden Markov Models
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and multimodal posterio…
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the param…
The paper connects ABC to GBI, suggesting ABC as a robustification strategy.
Bayesian optimization reduces computational effort in aircraft design optimization.
GBC methods compute expected utility without needing the model's density.
GSSBO reduces GP fitting time in Bayesian optimization.
Paper proposes a learning-based sparse Bayesian method for accurate off-grid DOA estimation.
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many approximations in implementation, introduce often-prohibitive computational overhead and limit the choi…
Extends Bayesian theory to handle complex interdependencies in multidimensional event spaces.
Proposes a new method to improve Bayesian computation accuracy using flexible classification.
BaMANI uses ensemble learning to improve Bayesian network inference.
GPU-accelerated BART speeds up Bayesian regression.
We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity analyses of complex problems, and hence allow global exploration of th…
The computational efficiency of approximate Bayesian computation (ABC) has been improved by using surrogate models such as Gaussian processes (GP). In one such promising framework the discrepancy between the simulated and observed data is modelled with a GP which is further used to form a model-based estimator for the …
A scalable parallel BO method for asynchronous settings.