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27 Jan 2023: This definition of tun-able variables is helpful to convert the constrained op-timization problem into an unconstrained optimizationproblem. ... ǩt = [Ka(xt, x1), Ka(xt, x2),. , Ka(xt, xn)].5 Themean of the predictive distribution P(fa(xt)|E, θ) canbe

27 Jan 2023: MN w,XU4QTgU4%Xc_ 4[VQT]4Q;]QZ46[dU N op_[VU VQTa%od[V%D[dQ][VUOva N dUDU_ ZX[ZgV N ... $ %'&. Op. tim. al. Mix. ing. Pro. po. rtio.

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1 Feb 2023: Geometric inverse problems. with emphasis on two dimensions. Gabriel P. Paternain, Mikko Salo, Gunther Uhlmann. iii. To our families and all who have supported us. This material has been published by Cambridge University Press & Assessment

13 Feb 2023: PYáN247L,I <7=Ka 246 2UI > I > 573T3 ã D < 6NL.246 abN= LL,I a L áI? ... DFE MÛUØUZJLNYZMSM 62TJ;á <7=Ka 5X6 a 24II > LÒ246 > Im246 VGM 24IÌà bN<Y< Im246.

13 Feb 2023: One op-tion to make the model more flexible is to add parameters to φ, which we thinkthey may be of importance. ... Maximizing the evidence using equation (2.24) is a nonlinear, non-convex op-timization problem.

13 Feb 2023: Bayesian Time Series Learning. with Gaussian Processes. Roger Frigola-AlcaldeDepartment of Engineering. St Edmund’s CollegeUniversity of Cambridge. August 2015. This dissertation is submitted for the degree ofDoctor of Philosophy. SUMMARY. The

13 Feb 2023: namely that if:. p. ([a. b. ])= N. ([µa. µb. ],. [Ka,a Ka,b. Kb,a Kb,b. ]), (1.17). then the means and covariances of marginals are simply the relevant subvectors ... and. submatrices of the joint mean and covariance respectively, i.e., p(a) = N(µa, Ka

22 Jun 2023: Preprint typeset in JHEP style - HYPER VERSION 2018. Gauge Theory. David Tong. Department of Applied Mathematics and Theoretical Physics,. Centre for Mathematical Sciences,. Wilberforce Road,. Cambridge, CB3 OBA, UK.