Results 21 to 30 of about 66,452 (307)
Normal Forms of Conditional Belief Bases Respecting Inductive Inference
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Normal forms of syntactic entities play an important role in many different areas in computer science. In this paper, we address the question of how to obtain normal forms and minimal normal forms of conditional belief bases in order to, e.g., ease ...
Christoph Beierle, Jonas Haldimann
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The Role of Trait Inferences in Evaluative Conditioning [PDF]
Collabra: Psychology, 2022 Evaluative Conditioning (EC) effect is a change in evaluative responding to a neutral stimulus (CS) due to its pairing with a valenced stimulus (US). Traditionally, EC effects are viewed as fundamentally different from per-suasion effects. Inspired by a propositional perspective to EC, four studies (N = 1,284) tested if, like persuasion effects, EC ...
Tal Moran +3 more
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Inference of cosmic-ray source properties by conditional invertible neural networks
European Physical Journal C: Particles and Fields, 2022 The inference of physical parameters from measured distributions constitutes a core task in physics data analyses. Among recent deep learning methods, so-called conditional invertible neural networks provide an elegant approach owing to their probability-
Teresa Bister +3 more
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Bayesian inference for the log-symmetric autoregressive conditional duration model
Anais da Academia Brasileira de Ciências, 2021 This paper adapts Hamiltonian Monte Carlo methods for application in log-symmetric autoregressive conditional duration models. These recent models are based on a class of log-symmetric distributions. In this class, it is possible to model both median and
JEREMIAS LEÃO +3 more
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Empirical likelihood inference for threshold autoregressive conditional heteroscedasticity model
Journal of Inequalities and Applications, 2021 This paper considers the parameter estimation problem of a first-order threshold autoregressive conditional heteroscedasticity model by using the empirical likelihood method.
Cuixin Peng, Zhiwen Zhao
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cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
Journal of Statistical Software, 2023 Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse ...
Luigi Augugliaro +3 more
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Inferring the conditional mean
CoRR, 2007 Consider a stationary real-valued time series $\{X_n\}_{n=0}^{\infty}$ with a priori unknown distribution. The goal is to estimate the conditional expectation $E(X_{n+1}|X_0,..., X_n)$ based on the observations $(X_0,..., X_n)$ in a pointwise consistent way. It is well known that this is not possible at all values of $n$.
Gusztáv Morvai, Benjamin Weiss 0002
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Conditional Inference in Small Sample Scenarios Using a Resampling Approach
Stats, 2021 This paper discusses a non-parametric resampling technique in the context of multidimensional or multiparameter hypothesis testing of assumptions of the Rasch model.
Clemens Draxler, Andreas Kurz
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Adaptive User Interfaces and the Use of Inference Methods
Computational Science and Techniques, 2021 Bayesian Networks are used to model a user's behaviour. There is not much research on the use of Frequentist Inference to accomplish this same task.
Rachelle Barrette, Ratvinder Grewal
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BMC Medical Research Methodology, 2017 Background Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data.
Justine B. Nasejje +3 more
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