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A Gaussian process is a Bayesian nonparametric model that treats the unknown function as a stochastic process, fully characterized by a mean function and a kernel function.
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10/15/2026
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UnverifiedThe method is applicable to both Gaussian process models and non-Gaussian process models.68% similarUnverifiedWhen the likelihood function is non-Gaussian (e.g., count data, binary data, or heavy-tailed distributions), the posterior distribution of the latent field becomes analytically intractable even when the prior is a Gaussian process.68% similarUnverifiedIn Gaussian process modeling, the kernel function encodes prior knowledge such as the smoothness and scale of the objective function.67% similarUnverifiedThe denoising sampling process of a diffusion model can be regarded as the solution of a stochastic differential equation, comprising a deterministic drift term and a stochastic diffusion term.67% similarUnverifiedPredictive Bayes reformulates the inference process as: using the model to make sequential predictions on unseen data, with these prediction sequences forming a martingale (a stochastic process whose expectation equals its current value).63% similar
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