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Bayesian Learning for Neural Networks [[electronic resource] /] / by Radford M. Neal
| Bayesian Learning for Neural Networks [[electronic resource] /] / by Radford M. Neal |
| Autore | Neal Radford M |
| Edizione | [1st ed. 1996.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 |
| Descrizione fisica | 1 online resource (204 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Statistics |
| Soggetto topico |
Probabilities
Statistics Artificial intelligence Computer simulation Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Artificial Intelligence Simulation and Modeling |
| ISBN | 1-4612-0745-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 1.1 Bayesian and frequentist views of learning -- 1.2 Bayesian neural networks -- 1.3 Markov chain Monte Carlo methods -- 1.4 Outline of the remainder of the book -- 2 Priors for Infinite Networks -- 2.1 Priors converging to Gaussian processes -- 2.2 Priors converging to non-Gaussian stable processes -- 2.3 Priors for nets with more than one hidden layer -- 2.4 Hierarchical models -- 3 Monte Carlo Implementation -- 3.1 The hybrid Monte Carlo algorithm -- 3.2 An implementation of Bayesian neural network learning -- 3.3 A demonstration of the hybrid Monte Carlo implementation -- 3.4 Comparison of hybrid Monte Carlo with other methods -- 3.5 Variants of hybrid Monte Carlo -- 4 Evaluation of Neural Network Models -- 4.1 Network architectures, priors, and training procedures -- 4.2 Tests of the behaviour of large networks -- 4.3 Tests of Automatic Relevance Determination -- 4.4 Tests of Bayesian models on real data sets -- 5 Conclusions and Further Work -- 5.1 Priors for complex models -- 5.2 Hierarchical Models — ARD and beyond -- 5.3 Implementation using hybrid Monte Carlo -- 5.4 Evaluating performance on realistic problems -- A Details of the Implementation -- A.1 Specifications -- A.1.1 Network architecture -- A.1.2 Data models -- A.1.3 Prior distributions for parameters and hyperparameters -- A.1.4 Scaling of priors -- A.2 Conditional distributions for hyperparameters -- A.2.1 Lowest-level conditional distributions -- A.2.2 Higher-level conditional distributions -- A.3 Calculation of derivatives -- A.3.1 Derivatives of the log prior density -- A.3.2 Log likelihood derivatives with respect to unit values -- A.3.3 Log likelihood derivatives with respect to parameters -- A.4 Heuristic choice of stepsizes -- A.5 Rejection sampling from the prior -- B Obtaining the software. |
| Record Nr. | UNINA-9910478911203321 |
Neal Radford M
|
||
| New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian Learning for Neural Networks [[electronic resource] /] / by Radford M. Neal
| Bayesian Learning for Neural Networks [[electronic resource] /] / by Radford M. Neal |
| Autore | Neal Radford M |
| Edizione | [1st ed. 1996.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 |
| Descrizione fisica | 1 online resource (204 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Statistics |
| Soggetto topico |
Probabilities
Statistics Artificial intelligence Computer simulation Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Artificial Intelligence Simulation and Modeling |
| ISBN | 1-4612-0745-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 1.1 Bayesian and frequentist views of learning -- 1.2 Bayesian neural networks -- 1.3 Markov chain Monte Carlo methods -- 1.4 Outline of the remainder of the book -- 2 Priors for Infinite Networks -- 2.1 Priors converging to Gaussian processes -- 2.2 Priors converging to non-Gaussian stable processes -- 2.3 Priors for nets with more than one hidden layer -- 2.4 Hierarchical models -- 3 Monte Carlo Implementation -- 3.1 The hybrid Monte Carlo algorithm -- 3.2 An implementation of Bayesian neural network learning -- 3.3 A demonstration of the hybrid Monte Carlo implementation -- 3.4 Comparison of hybrid Monte Carlo with other methods -- 3.5 Variants of hybrid Monte Carlo -- 4 Evaluation of Neural Network Models -- 4.1 Network architectures, priors, and training procedures -- 4.2 Tests of the behaviour of large networks -- 4.3 Tests of Automatic Relevance Determination -- 4.4 Tests of Bayesian models on real data sets -- 5 Conclusions and Further Work -- 5.1 Priors for complex models -- 5.2 Hierarchical Models — ARD and beyond -- 5.3 Implementation using hybrid Monte Carlo -- 5.4 Evaluating performance on realistic problems -- A Details of the Implementation -- A.1 Specifications -- A.1.1 Network architecture -- A.1.2 Data models -- A.1.3 Prior distributions for parameters and hyperparameters -- A.1.4 Scaling of priors -- A.2 Conditional distributions for hyperparameters -- A.2.1 Lowest-level conditional distributions -- A.2.2 Higher-level conditional distributions -- A.3 Calculation of derivatives -- A.3.1 Derivatives of the log prior density -- A.3.2 Log likelihood derivatives with respect to unit values -- A.3.3 Log likelihood derivatives with respect to parameters -- A.4 Heuristic choice of stepsizes -- A.5 Rejection sampling from the prior -- B Obtaining the software. |
| Record Nr. | UNINA-9910789348903321 |
|
Neal Radford M
|
||
| New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian Learning for Neural Networks / / by Radford M. Neal
| Bayesian Learning for Neural Networks / / by Radford M. Neal |
| Autore | Neal Radford M |
| Edizione | [1st ed. 1996.] |
| Pubbl/distr/stampa | New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 |
| Descrizione fisica | 1 online resource (204 p.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Statistics |
| Soggetto topico |
Probabilities
Statistics Artificial intelligence Computer simulation Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Artificial Intelligence Simulation and Modeling |
| ISBN | 1-4612-0745-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 1.1 Bayesian and frequentist views of learning -- 1.2 Bayesian neural networks -- 1.3 Markov chain Monte Carlo methods -- 1.4 Outline of the remainder of the book -- 2 Priors for Infinite Networks -- 2.1 Priors converging to Gaussian processes -- 2.2 Priors converging to non-Gaussian stable processes -- 2.3 Priors for nets with more than one hidden layer -- 2.4 Hierarchical models -- 3 Monte Carlo Implementation -- 3.1 The hybrid Monte Carlo algorithm -- 3.2 An implementation of Bayesian neural network learning -- 3.3 A demonstration of the hybrid Monte Carlo implementation -- 3.4 Comparison of hybrid Monte Carlo with other methods -- 3.5 Variants of hybrid Monte Carlo -- 4 Evaluation of Neural Network Models -- 4.1 Network architectures, priors, and training procedures -- 4.2 Tests of the behaviour of large networks -- 4.3 Tests of Automatic Relevance Determination -- 4.4 Tests of Bayesian models on real data sets -- 5 Conclusions and Further Work -- 5.1 Priors for complex models -- 5.2 Hierarchical Models — ARD and beyond -- 5.3 Implementation using hybrid Monte Carlo -- 5.4 Evaluating performance on realistic problems -- A Details of the Implementation -- A.1 Specifications -- A.1.1 Network architecture -- A.1.2 Data models -- A.1.3 Prior distributions for parameters and hyperparameters -- A.1.4 Scaling of priors -- A.2 Conditional distributions for hyperparameters -- A.2.1 Lowest-level conditional distributions -- A.2.2 Higher-level conditional distributions -- A.3 Calculation of derivatives -- A.3.1 Derivatives of the log prior density -- A.3.2 Log likelihood derivatives with respect to unit values -- A.3.3 Log likelihood derivatives with respect to parameters -- A.4 Heuristic choice of stepsizes -- A.5 Rejection sampling from the prior -- B Obtaining the software. |
| Record Nr. | UNINA-9910818803103321 |
|
Neal Radford M
|
||
| New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||