Laaj ak tontu ci xeetu Markov yu nëbbu

Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.

1. Luy 'lu nëbbu' ci xeetu Markov bu nëbbu bi?
   • mënu ñu seetlu nekkin yi, te dañu leen jëlee ci génne yi
   • Parametru model bi dañu sekkere
   • Seetlu yi dañu leen enkripte
   • Jaar-jaar yi dañuy nuru lu bari te kenn xamul
2. Lan mooy moomeeli Markov bi HMM jàpp ni moo am?
   • Etat yépp dañuy génne benn màndarga
   • Etat bi ci topp mingi aju ci etaa bi fi nekk
   • Seetlu yi moom seen bopp ci réew mi
   • Bépp etaa mën na nekk
3. Ban algorithm mooy gis benn xeetu nekkin bu nëbbu bi?
   • Algorithm biy yebal
   • Baum-Welch
   • Algorithm bu Viterbi
   • Wàcci degrade
4. Yan ci ñatti xeeti probabilite yi ñooy leeral bu baax HMM?
   • Lu njëkk, lu mëna am, ak firnde
   • Moyenne, variance, ak kovariance
   • Poid yiñ dugal, nëbb ak genn
   • Tolluwaayu njëkk, jàll, ak mënati génne
5. Lu algorithm bu Baum-Welch di def?
   • Jàng parametre HMM ci done jaaraleko ci xaar-maksimalisaasioŋ
   • Dafay enkripte toppalante seetlu bi
   • Wut benn yoon bi gëna am wërsëg
   • Xayma matrix transisioŋ bi ci anam wu tëju
6. Which Transformer feature can directly relate tokens at distant positions in a sequence?
   • An HMM emission table
   • Self-attention over positions in the input
   • A fixed single-state transition
   • A requirement to process only one token ever
7. What does the Forward algorithm compute for a sequence of observations?
   • Only the single most likely state path
   • The most likely parameter initialization
   • The total probability of the observations by summing over possible hidden-state paths
   • The mean of the emission probabilities
8. A hidden state emits a word or acoustic symbol. Which HMM probability describes that relation?
   • The initial-state probability
   • The transition probability
   • The discount factor
   • The emission probability