Pages that link to "Item:Q2642920"
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The following pages link to Approximate maximum a posteriori with Gaussian process priors (Q2642920):
Displaying 11 items.
- An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (Q68580) (← links)
- Kernel methods in system identification, machine learning and function estimation: a survey (Q462325) (← links)
- Variational problems in machine learning and their solution with finite elements (Q2796174) (← links)
- A matrix-free approach for solving the parametric Gaussian process maximum likelihood problem (Q2882787) (← links)
- A Partially Linear Model Using a Gaussian Process Prior (Q2943791) (← links)
- Posterior consistency for Gaussian process approximations of Bayesian posterior distributions (Q4600705) (← links)
- Γ -convergence of Onsager–Machlup functionals: I. With applications to maximum a posteriori estimation in Bayesian inverse problems (Q5019928) (← links)
- Altering Gaussian process to Student-<i>t</i> process for maximum distribution construction (Q5028003) (← links)
- Parametric Approximation Policy Iteration Algorithm Based on Gaussian Process (Q5165995) (← links)
- Maximum a posteriori estimators in ℓp are well-defined for diagonal Gaussian priors (Q6101037) (← links)
- On maximum a posteriori estimation with Plug \& Play priors and stochastic gradient descent (Q6155451) (← links)
