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[ספר][B] Bringing Bayesian models to life
Bringing Bayesian Models to Life empowers the reader to extend, enhance, and implement
statistical models for ecological and environmental data analysis. We open the black box …
statistical models for ecological and environmental data analysis. We open the black box …
Demographic and evolutionary consequences of hunting of wild birds
Hunting has a long tradition in human evolutionary history and remains a common leisure
activity or an important source of food. Herein, we first briefly review the literature on the …
activity or an important source of food. Herein, we first briefly review the literature on the …
Robust generalised Bayesian inference for intractable likelihoods
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a
likelihood, and can therefore be used to confer robustness against possible mis …
likelihood, and can therefore be used to confer robustness against possible mis …
Exponential-family models of random graphs
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …
for modeling dense and sparse random graphs with short-or long-tailed degree distributions …
Bayesian analysis of the ordinal Markov random field
Multivariate analysis using graphical models is rapidly gaining ground in psychology. In
particular, Markov random field (MRF) graphical models have become popular because …
particular, Markov random field (MRF) graphical models have become popular because …
Exponential random graph models for little networks
Statistical models for social networks have enabled researchers to study complex social
phenomena that give rise to observed patterns of relationships among social actors and to …
phenomena that give rise to observed patterns of relationships among social actors and to …
Generalized Bayesian inference for discrete intractable likelihood
Discrete state spaces represent a major computational challenge to statistical inference,
since the computation of normalization constants requires summation over large or possibly …
since the computation of normalization constants requires summation over large or possibly …
Unbiased Markov chain Monte Carlo for intractable target distributions
L Middleton, G Deligiannidis, A Doucet, PE Jacob - 2020 - projecteuclid.org
Performing numerical integration when the integrand itself cannot be evaluated point-wise is
a challenging task that arises in statistical analysis, notably in Bayesian inference for models …
a challenging task that arises in statistical analysis, notably in Bayesian inference for models …
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
This survey gives an overview of Monte Carlo methodologies using surrogate models, for
dealing with densities that are intractable, costly, and/or noisy. This type of problem can be …
dealing with densities that are intractable, costly, and/or noisy. This type of problem can be …
On the normalized power prior
The power prior is a popular tool for constructing informative prior distributions based on
historical data. The method consists of raising the likelihood to a discounting factor in order …
historical data. The method consists of raising the likelihood to a discounting factor in order …