Results 21 to 30 of about 787,887 (241)
Human-Centric Intelligent Systems, 2023 Understanding the dynamics of a system is crucial in various scientific and engineering domains. Machine learning techniques have been employed to learn state transition rules from observed time-series data. However, these data often contain sequences of
Koji Watanabe, Katsumi Inoue
doaj +1 more source
Causal Structure Learning Algorithm Based on Cascade Additive Noise Model [PDF]
Jisuanji gongcheng, 2022 The existing cascade nonlinear Additive Noise Model(ANM) can infer the causal direction of hidden intermediate variables, but fail to deal with global structure search and equivalence class recognition in the case of causal network learning that includes
QIAO Jie, CAI Ruichu, HAO Zhifeng
doaj +1 more source
Bianchi Cosmologies: New Variables and a Hidden Supersymmetry [PDF]
, 1993 We find a supersymmetrization of the Bianchi IX cosmology in terms of Ashtekar's new variables. This provides a framework for connecting the recent results of Graham and those of Ryan and Moncrief for quantum states of this model.
Apartado Postal E +4 more
core +4 more sources
Toy quantum mechanics using hidden variables
Discrete Dynamics in Nature and Society, 2004 An original model of toy quantum mechanics that uses hidden variables but does not violate the well-known Bell theorem is proposed.
Pavel V. Kurakin, George G. Malinetskii
doaj +1 more source
State Space Modeling with Non-Negativity Constraints Using Quadratic Forms
Mathematics, 2021 State space model representation is widely used for the estimation of nonobservable (hidden) random variables when noisy observations of the associated stochastic process are available.
Ourania Theodosiadou, George Tsaklidis
doaj +1 more source
Parameter Identifiability of Discrete Bayesian Networks with Hidden Variables
Journal of Causal Inference, 2015 Identifiability of parameters is an essential property for a statistical model to be useful in most settings. However, establishing parameter identifiability for Bayesian networks with hidden variables remains challenging.
Allman Elizabeth S. +3 more
doaj +1 more source
Machine learning of hidden variables in multiscale fluid simulation
Machine Learning: Science and Technology, 2023 Solving fluid dynamics equations often requires the use of closure relations that account for missing microphysics. For example, when solving equations related to fluid dynamics for systems with a large Reynolds number, sub-grid effects become important ...
Archis S Joglekar, Alexander G R Thomas
doaj +1 more source
Hidden Markov Model Based on Logistic Regression
Mathematics, 2023 A hidden Markov model (HMM) is a useful tool for modeling dependent heterogeneous phenomena. It can be used to find factors that affect real-world events, even when those factors cannot be directly observed.
Byeongheon Lee, Joowon Park, Yongku Kim
doaj +1 more source
Consistent histories, quantum truth functionals, and hidden variables [PDF]
, 1999 A central principle of consistent histories quantum theory, the requirement that quantum descriptions be based upon a single framework (or family), is employed to show that there is no conflict between consistent histories and a no-hidden-variables ...
Bassi +10 more
core +2 more sources
Learning with hidden variables
, 2015 Learning and inferring features that generate sensory input is a task continuously performed by cortex. In recent years, novel algorithms and learning rules have been proposed that allow neural network models to learn such features from natural images ...
Roudi, Yasser, Taylor, Graham
core +1 more source