Zobrazeno 1 - 10
of 523
pro vyhledávání: '"Nott, David"'
Joint modeling of different data sources in decision-making processes is crucial for understanding decision dynamics in consumer behavior models. Sequential Sampling Models (SSMs), grounded in neuro-cognitive principles, provide a systematic approach
Externí odkaz:
http://arxiv.org/abs/2409.01735
We propose a novel distributional regression model for a multivariate response vector based on a copula process over the covariate space. It uses the implicit copula of a Gaussian multivariate regression, which we call a ``regression copula''. To all
Externí odkaz:
http://arxiv.org/abs/2401.11804
Mixtures of linear mixed models are widely used for modelling longitudinal data for which observation times differ between subjects. In typical applications, temporal trends are described using a basis expansion, with basis coefficients treated as ra
Externí odkaz:
http://arxiv.org/abs/2311.07156
In copula models the marginal distributions and copula function are specified separately. We treat these as two modules in a modular Bayesian inference framework, and propose conducting modified Bayesian inference by "cutting feedback". Cutting feedb
Externí odkaz:
http://arxiv.org/abs/2310.03521
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
The Bayesian Synthetic Likelihood (BSL) method is a widely-used tool for likelihood-free Bayesian inference. This method assumes that some summary statistics are normally distributed, which can be incorrect in many applications. We propose a transfor
Externí odkaz:
http://arxiv.org/abs/2305.14746
Bayesian inference is a powerful tool for combining information in complex settings, a task of increasing importance in modern applications. However, Bayesian inference with a flawed model can produce unreliable conclusions. This review discusses app
Externí odkaz:
http://arxiv.org/abs/2305.08429
Even though dropout is a popular regularization technique, its theoretical properties are not fully understood. In this paper we study dropout regularization in extended generalized linear models based on double exponential families, for which the di
Externí odkaz:
http://arxiv.org/abs/2305.06625
Bayesian statistics is concerned with conducting posterior inference for the unknown quantities in a given statistical model. Conventional Bayesian inference requires the specification of a probabilistic model for the observed data, and the construct
Externí odkaz:
http://arxiv.org/abs/2305.05120
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This paper considers ap
Externí odkaz:
http://arxiv.org/abs/2302.03348