Kediska Habraaca Gaussian

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Habka Gaussian wuxuu qeexayaa qaybinta itimaalka maxaa?
   • Hawlaha
   • Dhibcaha xogta shaqsiga kaliya
   • Calaamadaha fasalka Discrete
   • Miisaanka shabakada neerfaha
2. Beyond a point estimate, what does a Gaussian Process return under its chosen kernel and noise assumptions?
   • Only an exact parameter vector
   • A posterior predictive distribution with an uncertainty estimate
   • A guaranteed calibrated interval under any model mismatch
   • A deterministic label with no variance
3. Doorkee ayuu kernel-ku ka ciyaaraa GP-ga?
   • Waxay calaamadisaa xogta tababarka
   • Waxay meesha ka saaraysaa baahida xogta tababarka
   • Waxay qeexaysaa sida wax-soo-saarka la midka ah ay u yihiin wax-soo-gelinta dhow
   • Waxay dejisaa heerka waxbarashada
4. Waa maxay sababta hababka Gaussian caadiga ah ay ula halgamaan xog-ururinta aadka u weyn?
   • Ma matali karaan hawlo aan toos ahayn
   • Waxay u baahan yihiin fasallo calaamadeysan
   • Waxay iska indhatiraan xogta tababarka gabi ahaanba
   • U rogida matrixka kernel-ka waxay ku kacaysaa qiyaastii wakhtiga n-cubed
5. Sidee loo doortaa cabbirada GP-ga sida cabbirka dhererka?
   • Iyada oo la kordhinayo suurtogalnimada marginal
   • Male-awaal aan toos ahayn
   • Iyadoo la yareynayo tirada dhibcaha xogta
   • Had iyo jeer waa kuwo go'an oo aan waligood la hagaajin
6. Waa kuwee farsamada caanka ah ee geeddi-socodka Gaussian qayb muhiim ah?
   • Kala soocida sawirka convolutional
   • Hagaajinta Bayesian ee hagaajinta hyperparameter
   • Calaamadaynta qoraalka
   • Dib u faafinta iyada oo loo marayo shabakado qoto dheer
7. Which kernel is described in the guide as smoother than the Matérn alternative?
   • A unigram frequency kernel
   • A transition-probability kernel
   • The RBF (squared exponential) kernel
   • The Viterbi kernel
8. Why can a GP’s predictive uncertainty widen far from training observations?
   • The model adds more observed labels automatically
   • The mean function becomes a neural network
   • The kernel matrix becomes smaller than zero
   • There is less nearby data constraining the function under the selected covariance assumptions