Results 71 to 80 of about 1,895,431 (338)
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
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
Molecular Oncology, EarlyView.Elevated level of cholesterol is positively correlated to prostate cancer development and disease severity. Cholesterol‐lowering drugs, such as statins, are demonstrated to inhibit prostate cancer. VNPP433‐3β interrupts multiple signaling and metabolic pathways, including cholesterol biosynthesis, AR‐mediated transcription of several oncogenes, mRNA 5′
Retheesh S. Thankan +10 more
wiley +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
Generating and Sampling Orbits for Lifted Probabilistic Inference [PDF]
, 2019 A key goal in the design of probabilistic inference algorithms is identifying and exploiting properties of the distribution that make inference tractable.
Broeck, Guy Van den +2 more
core +1 more source
Statistical Science, 2011 Don Fraser has given an interesting account of the agreements and disagreements between Bayesian posterior probabilities and confidence levels. In this comment I discuss some cases where the lack of such agreement is extreme. I then discuss a few cases where it is possible to have Bayes procedures with frequentist validity. Such frequentist-Bayesian—or
openaire +3 more sources
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
Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma
Molecular Oncology, EarlyView.This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu +15 more
wiley +1 more source
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