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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 electronic resource (508 p.) |
| Soggetto topico |
Research & information: general
Mathematics & science |
| Soggetto non controllato |
bifurcation
dynamical systems Edward–Sokal coupling mean-field Kullback–Leibler divergence variational inference Bayesian statistics machine learning variational approximations PAC-Bayes expectation-propagation Markov chain Monte Carlo Langevin Monte Carlo sequential Monte Carlo Laplace approximations approximate Bayesian computation Gibbs posterior MCMC stochastic gradients neural networks Approximate Bayesian Computation differential evolution Markov kernels discrete state space ergodicity Markov chain probably approximately correct variational Bayes Bayesian inference Markov Chain Monte Carlo Sequential Monte Carlo Riemann Manifold Hamiltonian Monte Carlo integrated nested laplace approximation fixed-form variational Bayes stochastic volatility network modeling network variability Stiefel manifold MCMC-SAEM data imputation Bethe free energy factor graphs message passing variational free energy variational message passing approximate Bayesian computation (ABC) differential privacy (DP) sparse vector technique (SVT) Gaussian particle flow variable flow Langevin dynamics Hamilton Monte Carlo non-reversible dynamics control variates thinning meta-learning hyperparameters priors online learning online optimization gradient descent statistical learning theory PAC–Bayes theory deep learning generalisation bounds Bayesian sampling Monte Carlo integration PAC-Bayes theory no free lunch theorems sequential learning principal curves data streams regret bounds greedy algorithm sleeping experts entropy robustness statistical mechanics complex systems |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576874903321 |
Alquier Pierre
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Overcoming Data Scarcity in Earth Science
| Overcoming Data Scarcity in Earth Science |
| Autore | Etcheverry Venturini Lorena |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (94 p.) |
| Soggetto non controllato |
geophysical monitoring
data scarcity missing data climate extreme indices (CEIs) rule extraction Dataset Licensedatabase data assimilation data imputation support vector machines environmental observations multi-class classification earth-science data remote sensing magnetotelluric monitoring soil texture calculator machine learning ClimPACT invasive species species distribution modeling 3D-Var ensemble learning data quality water quality microhabitat k-Nearest Neighbors Expert Team on Climate Change Detection and Indices (ETCCDI) decision trees processing attribute reduction Expert Team on Sector-specific Climate Indices (ET-SCI) core attribute rough set theory GLDAS arthropod vector environmental modeling statistical methods |
| ISBN | 3-03928-211-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404080803321 |
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Etcheverry Venturini Lorena
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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