Maswali ya Siri ya Miundo ya Markov

Majibu na maelezo huonekana unapojibu maswali.

1. Je, 'iliyofichwa' katika Mfano wa Siri ya Markov inarejelea nini?
   • Majimbo hayaonekani na yanakisiwa kutoka kwa matokeo
   • Vigezo vya mfano ni siri
   • Uchunguzi umesimbwa kwa njia fiche
   • Mabadiliko ni ya nasibu na hayajulikani
2. Ni mali gani ya Markov iliyochukuliwa na HMM?
   • Majimbo yote hutoa ishara sawa
   • Hali inayofuata inategemea tu hali ya sasa
   • Uchunguzi haujitegemei na majimbo
   • Kila jimbo lina uwezekano sawa
3. Ni algorithm gani hupata mlolongo mmoja unaowezekana zaidi wa majimbo yaliyofichwa?
   • Algorithm ya Mbele
   • Baum-Welch
   • Algorithm ya Viterbi
   • Kushuka kwa gradient
4. Ni seti gani tatu za uwezekano zinabainisha kikamilifu HMM?
   • Kabla, uwezekano, na ushahidi
   • Maana, tofauti, na ushirikiano
   • Ingizo, zilizofichwa, na uzani wa pato
   • Hali ya awali, mpito, na uwezekano wa utoaji
5. Je! Algorithm ya Baum-Welch hufanya nini?
   • Hujifunza vigezo vya HMM kutoka kwa data kupitia uboreshaji wa matarajio
   • Husimba kwa njia fiche mfuatano wa uchunguzi
   • Hupata njia moja inayowezekana zaidi
   • Huhesabu matriki halisi ya mpito katika hali iliyofungwa
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