dm.cs.tu-dortmund.de/en/mlbits/sequential-models-maximum-entropy-models/
Maximum Entropy Markov Models (MEMM) – Lecture Notes
\(y_2\) , \(y_2\rightarrow y_2\) is most likely
\(P(\textcolor[RGB]{132,184,24}{y_1,y_1,y_1,y_1}) = .4\cdot .45 \cdot .5 = 0.09\)
\(P(\textcolor[RGB]{246,180,63}{y_2,y_2,y_2,y_2}) = .3\cdot .3 \cdot .3 = [...] 170}{y_1,y_2,y_1,y_2}) = .6\cdot .2 \cdot .5 = 0.06\)
\(P(\textcolor[RGB]{191,2,127}{y_1,y_1,y_2,y_2}) = .4\cdot .55 \cdot .3 = 0.066\)
Most likely path: always \(y_1\)
Average outgoing weight of \(y_2\) smaller …
