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#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}

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

?

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}

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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>

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**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

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>

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

status | not learned | measured difficulty | 37% [default] | last interval [days] | |||
---|---|---|---|---|---|---|---|

repetition number in this series | 0 | memorised on | scheduled repetition | ||||

scheduled repetition interval | last repetition or drill |

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