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Existence of Gaussian processesTheorem◮ Let M be a topological space.◮ Let C : M × M be positive semi-definite.◮ Then there exists a Gaussian process on f : M → R with meanzero and covariance function C.◮ Furthermore, f has a representation of the formf (t) = ∑ j ξ jϕ j (t), and if f is a.s. continuous then the sumconverges uniformly, a.s., on compacts.
Existence of Gaussian processesTheorem◮ Let M be a topological space.◮ Let C : M × M be positive semi-definite.◮ Then there exists a Gaussian process on f : M → R with meanzero and covariance function C.◮ Furthermore, f has a representation of the formf (t) = ∑ j ξ jϕ j (t), and if f is a.s. continuous then the sumconverges uniformly, a.s., on compacts.Corollary◮ If there is justice in the world (smoothness and summability)ḟ (t) =∂ ∂t f (t) = ∑ jξ j ˙ϕ j (t),and so if f is Gaussian, so is ḟ . Where ḟ is any derivative on nice M.
Page 4 and 5: Our heroesMarc KacStephen O. Rice19
Page 6 and 7: Real roots of algebraic equations (
Page 10 and 11: Zeroes of complex polynomialsf (z)
Page 12 and 13: Zeroes of complex polynomialsf (z)
Page 14 and 15: Thinking more generally:1: f : R N
Page 16 and 17: Thinking more generally:1: f : R N
Page 18 and 19: The original (non-specific) Rice fo
Page 30 and 31: Constructing Gaussian Processes◮
Page 32 and 33: Constructing Gaussian Processes◮
Page 36 and 37: Existence of Gaussian processesTheo
Page 38 and 39: Example: The Brownian sheet on R NE
Page 40 and 41: Example: The Brownian sheet on R NE
Page 42 and 43: Constant variance Gaussian processe
Page 50 and 51: Gaussian Kac-Rice with no simplific
Page 52 and 53: Gaussian Kac-Rice with no simplific
Page 54 and 55: Some pertinent thoughts◮ Real roo
Page 56 and 57: Some pertinent thoughts◮ Real roo
Page 58 and 59: The Kac-Rice “Metatheorem”
Page 60 and 61: The Kac-Rice “Metatheorem”◮ T
Page 62 and 63: The Kac-Rice “Metatheorem”◮ T
Page 68 and 69: The Kac-Rice Conditions (the fine p
Page 70 and 71: Higher (factorial) moments◮ Facto
Page 72 and 73: The Gaussian case: What can/can’t
Page 80 and 81: The Gaussian-related casef (t) = (f
Page 82 and 83: The Gaussian-related casef (t) = (f
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The Gaussian Kinematic Formula (GKF
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The perturbed-Gaussian case◮ A ph
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Applications I: Exceedence probabil
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Applications I: Exceedence probabil
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Applications II: Local maxima on th
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Applications III: Local maxima on M
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Applications III: Local maxima on M
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Applications IV: Longuet-Higgins an
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Applications V: Higher moments and
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Applications VI: Poisson limits and
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Applications VI: Poisson limits and
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Applications VII: Eigenvalues of ra
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Applications VII: Eigenvalues of ra
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Appendix I: The canonical Gaussian
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Exceedence probabilities for canoni
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◮ We needP { sup〈U, t〉 ≥ u/
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Appendix II: Stationary and isotrop
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Appendix II: Stationary and isotrop
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◮ Elementary considerations give{
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◮ Elementary considerations give{
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Appendix III: Regularity of Gaussia
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Appendix III: Regularity of Gaussia
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Special cases of the entropy result
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Special cases of the entropy result
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Appendix IV: Borell-Tsirelson inequ
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Appendix IV: Borell-Tsirelson inequ
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