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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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Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods
| Nonparametric Statistical Inference with an Emphasis on Information-Theoretic Methods |
| Autore | Mielniczuk Jan |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (226 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Mechanical engineering & materials |
| Soggetto non controllato |
high-dimensional time series
nonstationarity network estimation change points kernel estimation high-dimensional regression loss function random predictors misspecification consistent selection subgaussianity generalized information criterion robustness statistical learning theory information theory entropy parameter estimation learning systems privacy prediction methods misclassification risk model misspecification penalized estimation supervised classification variable selection consistency archimedean copula consistency estimation extreme-value copula tail dependency multivariate analysis conditional mutual information CMI information measures nonparametric variable selection criteria gaussian mixture conditional infomax feature extraction CIFE joint mutual information criterion JMI generative tree model Markov blanket minimum distance estimation maximum likelihood estimation influence functions adaptive splines B-splines right-censored data semiparametric regression synthetic data transformation time series |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576873203321 |
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Mielniczuk Jan
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| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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