#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)).
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Viterbi algorithm - Wikipedially 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
Summary
| status | not read | | reprioritisations | |
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| last reprioritisation on | | | suggested re-reading day | |
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| started reading on | | | finished reading on | |
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Details