Results 21 to 30 of about 377,664 (139)
, 2010 In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents.
Caticha, Ariel
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Scalable Inference for Markov Processes with Intractable Likelihoods
, 2014 Bayesian inference for Markov processes has become increasingly relevant in recent years. Problems of this type often have intractable likelihoods and prior knowledge about model rate parameters is often poor.
Gillespie, Colin S. +2 more
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, 1966 Bayes' original paper "Essay Towards Solving a Problem in the Doctrine of Change" was published in Philosophical Transactions of the Royal Society, 1763. Over 200 years, Bayes' Concepts have survived numerous critical onslaughts. Even though Bayesian Inference is still regarded as being somewhat unorthodox, it is becoming more generally accepted each ...
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Bayesian Inference for Hawkes Processes [PDF]
Methodology and Computing in Applied Probability, 2011 The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional intensity function, while the second approach is based on an ...
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PAC-Bayesian Theory Meets Bayesian Inference [PDF]
, 2016 We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the Bayesian marginal likelihood. That is, for the negative log-likelihood loss function, we show that the minimization of PAC-Bayesian generalization risk bounds maximizes the ...
Bach, Francis +3 more
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Bayesian frequentist hybrid inference
, 2009 Bayesian and frequentist methods differ in many aspects, but share some basic optimality properties. In practice, there are situations in which one of the methods is more preferred by some criteria.
Yuan, Ao
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A Survey on Bayesian Deep Learning
, 2020 A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty.
Wang, Hao, Yeung, Dit-Yan
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Bayesian Inference under Cluster Sampling with Probability Proportional to Size [PDF]
, 2017 Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling.
Little RJA +3 more
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NeuroImage, 2012 Bayesian inference has taken FMRI methods research into areas that frequentist statistics have struggled to reach. In this article we will consider some of the early forays into Bayes and what motivated its use. We shall see the impact that Bayes has had on haemodynamic modelling, spatial modelling, group analysis, model selection and brain ...
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Robust approximate Bayesian inference
, 2019 We discuss an approach for deriving robust posterior distributions from $M$-estimating functions using Approximate Bayesian Computation (ABC) methods.
Ruli, Erlis +2 more
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