Results 91 to 100 of about 2,253,559 (287)
Computational Causal Inference [PDF]
arXiv, 2020 We introduce computational causal inference as an interdisciplinary field across causal inference, algorithms design and numerical computing. The field aims to develop software specializing in causal inference that can analyze massive datasets with a variety of causal effects, in a performant, general, and robust way.
arxiv
Molecular Oncology, EarlyView.The LEXOVE prospective study evaluated plasma cell‐free extracellular vesicle (cfEV) dynamics using Bradford assay and dynamic light scattering in metastatic non‐small cell lung cancer patients undergoing first‐line treatments, correlating a ∆cfEV < 20% with improved median progression‐free survival in responders versus non‐responders.
Valerio Gristina +17 more
wiley +1 more source
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language [PDF]
Journal Of Artificial Intelligence Research, Volume 47, pages
393-439, 2013, 2014 Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once per group, as opposed to once per variable.
arxiv +1 more source
Hardware Design for Autonomous Bayesian Networks
Frontiers in Computational Neuroscience, 2021 Directed acyclic graphs or Bayesian networks that are popular in many AI-related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits built out of probabilistic bits (p-bits), analogous to binary stochastic ...
Rafatul Faria +3 more
doaj +1 more source
Online but Accurate Inference for Latent Variable Models with Local Gibbs Sampling [PDF]
, 2016 We study parameter inference in large-scale latent variable models. We first propose an unified treatment of online inference for latent variable models from a non-canonical exponential family, and draw explicit links between several previously proposed ...
Bach, Francis, Dupuy, Christophe
core +3 more sources
Variational Inference for GARCH-family Models [PDF]
arXiv, 2023 The Bayesian estimation of GARCH-family models has been typically addressed through Monte Carlo sampling. Variational Inference is gaining popularity and attention as a robust approach for Bayesian inference in complex machine learning models; however, its adoption in econometrics and finance is limited.
arxiv
Inference on counterfactual distributions [PDF]
SSRN Electronic Journal, 2009 Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider consist of ceteris paribus changes in either the distribution of
Chernozhukov, Victor V. +2 more
openaire +12 more sources
Fast methods for training Gaussian processes on large datasets [PDF]
Royal Society Open Science, 2016 Gaussian process regression (GPR) is a non-parametric Bayesian technique for interpolating or fitting data. The main barrier to further uptake of this powerful tool rests in the computational costs associated with the matrices which arise when dealing ...
C. J. Moore +3 more
doaj +1 more source
Spine-local Type Inference [PDF]
arXiv, 2018 We present spine-local type inference, a partial type inference system for inferring omitted type annotations for System F terms based on local type inference. Local type inference relies on bidirectional inference rules to propagate type information into and out of adjacent nodes of the AST and restricts type-argument inference to occur only within a ...
arxiv