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Applying the Free-Energy Principle to Complex Adaptive Systems
| Applying the Free-Energy Principle to Complex Adaptive Systems |
| Autore | Badcock Paul |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (214 p.) |
| Soggetto topico |
Computer science
Information technology industries |
| Soggetto non controllato |
active inference
adaptive robots affect control theory agency agent-based model allostatic (hub) overload apoptosis Bayesian Bayesian brain Bayesian inference cancer niches cascading failure cluster variation method cognitive-affective development cognitivism collective intelligence complex adaptive systems computational model consciousness critical slowing down cybernetics disease disorder embodiment emotion emotions enactivism feelings filtering free energy free energy principle Free Energy Principle free will generative model generative models hierarchical control systems intelligence intentionality Kikuchi approximations master equations mental causation message passing metabolism metastasis model-based control multiscale systems n/a neurotechnology non-equilibrium permutation entropy POMDP readiness potentials representations sociology stochastic stress uncertainty |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910674391803321 |
Badcock Paul
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Approximate Bayesian Inference
| Approximate Bayesian Inference |
| Autore | Alquier Pierre |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (508 p.) |
| Soggetto topico |
Mathematics and Science
Research and information: general |
| Soggetto non controllato |
approximate Bayesian computation
Approximate Bayesian Computation approximate Bayesian computation (ABC) Bayesian inference Bayesian sampling Bayesian statistics Bethe free energy bifurcation complex systems control variates data imputation data streams deep learning differential evolution differential privacy (DP) discrete state space dynamical systems Edward-Sokal coupling entropy ergodicity expectation-propagation factor graphs fixed-form variational Bayes Gaussian generalisation bounds Gibbs posterior gradient descent greedy algorithm Hamilton Monte Carlo hyperparameters integrated nested laplace approximation Kullback-Leibler divergence Langevin dynamics Langevin Monte Carlo Laplace approximations machine learning Markov chain Markov chain Monte Carlo Markov Chain Monte Carlo Markov kernels MCMC MCMC-SAEM mean-field message passing meta-learning Monte Carlo integration network modeling network variability neural networks no free lunch theorems non-reversible dynamics online learning online optimization PAC-Bayes PAC-Bayes theory particle flow principal curves priors probably approximately correct regret bounds Riemann Manifold Hamiltonian Monte Carlo robustness sequential learning sequential Monte Carlo Sequential Monte Carlo sleeping experts sparse vector technique (SVT) statistical learning theory statistical mechanics Stiefel manifold stochastic gradients stochastic volatility thinning variable flow variational approximations variational Bayes variational free energy variational inference variational message passing |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576874903321 |
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Alquier Pierre
|
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||