Bayesian nonparametrics via neural networks
β Scribed by Herbert K. H. Lee
- Publisher
- Society for Industrial and Applied Mathematics, Society for Industrial and Applied Mathematics; ASA, American Statistical Association
- Year
- 2004
- Tongue
- English
- Leaves
- 107
- Series
- ASA-SIAM series on statistics and applied probability
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Subjects
ΠΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠ° ΠΈ Π²ΡΡΠΈΡΠ»ΠΈΡΠ΅Π»ΡΠ½Π°Ρ ΡΠ΅Ρ Π½ΠΈΠΊΠ°;ΠΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ;ΠΠ΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ;
π SIMILAR VOLUMES
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating t
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating t
<p>Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network