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Bayesian Reconstruction of Missing Observations [PDF]
, 2015 We focus on an interpolation method referred to Bayesian reconstruction in this paper. Whereas in standard interpolation methods missing data are interpolated deterministically, in Bayesian reconstruction, missing data are interpolated probabilistically ...
Kataoka, Shun +2 more
core +3 more sources
Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10
Virus Evolution, 2018 The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data.
M. Suchard +5 more
semanticscholar +1 more source
COMPARISON OF FREQUENTIST AND BAYESIAN APPROACHES ON SAMPLE SIZE: METHODOLOGIC STUDY
Sabiad, 2023 Objective: In the present study, we aimed to evaluate the effects of sample size on results of study by using frequentist and Bayesian approaches.Material and Methods: The small sample consisted of 32 patients with ischemic heart disease (IHD) and 37 ...
Cennet Yıldız +3 more
doaj +1 more source
Tehran Stock Exchange Return Forecasting: Comparison of Bayesian, Exponential Smoothing and Box Jenkins Approaches [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2022 Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental ...
Mojtaba Rostami +1 more
doaj +1 more source
BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
bioRxiv, 2018 Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data.
R. Bouckaert +24 more
semanticscholar +1 more source
How Bayesian should Bayesian optimisation be? [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021 Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the GP hyperparameters are fit to previously-evaluated data by maximising the marginal likelihood.
De Ath, George +2 more
openaire +2 more sources
The American Statistician, 2019 Although no universally accepted definition of causality exists, in practice one is often faced with the question of statistically assessing causal relationships in different settings. We present a uniform general approach to causality problems derived from the axiomatic foundations of the Bayesian statistical framework.
Pierre Baldi, Babak Shahbaba
openaire +4 more sources
Bayesian games with a continuum of states [PDF]
, 2017 We show that every Bayesian game with purely atomic types has a measurable Bayesian equilibrium when the common knowl- edge relation is smooth. Conversely, for any common knowledge rela- tion that is not smooth, there exists a type space that
Hellman, Ziv, Levy, Yehuda John
core +1 more source
brms: An R Package for Bayesian Multilevel Models Using Stan
, 2017 The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit - among others - linear, robust linear, binomial, Poisson,
P. Bürkner
semanticscholar +1 more source
Machine Learning: Science and Technology, 2023 Abstract In this note we present a fully information theoretic approach to renormalization inspired by Bayesian statistical inference, which we refer to as Bayesian renormalization. The main insight of Bayesian renormalization is that the Fisher metric defines a correlation length that plays the role of an emergent renormalization group (
David S Berman +2 more
openaire +4 more sources