Edited, memorised or added to reading queue

on 12-May-2018 (Sat)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

Annotation 2965643332876

#viterbi-algorithm
This algorithm generates a path \({\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})}\), which is a sequence of states \({\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}}\) that generate the observations \({\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})}\) with \({\displaystyle y_{n}\in O=\{o_{1},o_{2},\dots ,o_{N}\}}\) (\(N\) being the count of observations (observation space, see below)).
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

Viterbi algorithm - Wikipedia
lly needs to, and usually manages to get away with doing a lot less work (in software) than the ordinary Viterbi algorithm for the same result—however, it is not so easy [clarification needed] to parallelize in hardware. Pseudocode[edit] <span>This algorithm generates a path X = ( x 1 , x 2 , … , x T ) {\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})} , which is a sequence of states x n ∈ S = { s 1 , s 2 , … , s K } {\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}} that generate the observations Y = ( y 1 , y 2 , … , y T ) {\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})} with y n ∈ O = { o 1 , o 2 , … , o N } {\displaystyle y_{n}\in O=\{o_{1},o_{2},\dots ,o_{N}\}} ( N {\displaystyle N} being the count of observations (observation space, see below)). Two 2-dimensional tables of size K × T {\displaystyle K\times T} are constructed: Each element



Flashcard 2965644905740

Tags
#viterbi-algorithm
Question
This algorithm generates a path \({\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})}\), which is a sequence of states \({\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}}\) that generate the observations \({\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})}\) with \({\displaystyle y_{n}\in O=\{o_{1},[...],\dots ,o_{N}\}}\) (\(N\) being the count of observations (observation space, see below)).
Answer
o_{2}

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition

Parent (intermediate) annotation

Open it
x_{1},x_{2},\ldots ,x_{T})}\), which is a sequence of states \({\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}}\) that generate the observations \({\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})}\) with \({\displaystyle y_{n}\in O=\{o_{1},<span>o_{2},\dots ,o_{N}\}}\) (\(N\) being the count of observations (observation space, see below)). <span><body><html>

Original toplevel document

Viterbi algorithm - Wikipedia
lly needs to, and usually manages to get away with doing a lot less work (in software) than the ordinary Viterbi algorithm for the same result—however, it is not so easy [clarification needed] to parallelize in hardware. Pseudocode[edit] <span>This algorithm generates a path X = ( x 1 , x 2 , … , x T ) {\displaystyle X=(x_{1},x_{2},\ldots ,x_{T})} , which is a sequence of states x n ∈ S = { s 1 , s 2 , … , s K } {\displaystyle x_{n}\in S=\{s_{1},s_{2},\dots ,s_{K}\}} that generate the observations Y = ( y 1 , y 2 , … , y T ) {\displaystyle Y=(y_{1},y_{2},\ldots ,y_{T})} with y n ∈ O = { o 1 , o 2 , … , o N } {\displaystyle y_{n}\in O=\{o_{1},o_{2},\dots ,o_{N}\}} ( N {\displaystyle N} being the count of observations (observation space, see below)). Two 2-dimensional tables of size K × T {\displaystyle K\times T} are constructed: Each element