De Bortoli et al., 2019 - Google Patents
Efficient stochastic optimisation by unadjusted langevin monte carlo. application to maximum marginal likelihood and empirical bayesian estimationDe Bortoli et al., 2019
View PDF- Document ID
- 4485154104980896848
- Author
- De Bortoli V
- Durmus A
- Pereyra M
- Vidal A
- Publication year
- Publication venue
- arXiv preprint arXiv:1906.12281
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Snippet
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in problems with missing or incomplete data, and in parametric empirical Bayesian estimation …
- 238000000034 method 0 abstract description 24
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- G06T2207/00—Indexing scheme for image analysis or image enhancement
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- G06T2207/30004—Biomedical image processing
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- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
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- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
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- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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