Viterbi algorithm
E252270
The Viterbi algorithm is a dynamic programming method used to find the most likely sequence of hidden states in probabilistic models such as Hidden Markov Models, widely applied in fields like digital communications, speech recognition, and bioinformatics.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Viterbi algorithm canonical | 4 |
| Viterbi path | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2268046 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viterbi algorithm Context triple: [Andrew Viterbi, notableWork, Viterbi algorithm]
-
A.
Berlekamp–Massey algorithm
The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
-
B.
Thompson's algorithm
Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
-
C.
Marzullo's algorithm
Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
-
D.
Knuth–Morris–Pratt algorithm
The Knuth–Morris–Pratt algorithm is a classic linear-time string-searching algorithm that efficiently finds occurrences of a pattern within a text by precomputing a prefix function to avoid redundant comparisons.
-
E.
Thompson's algorithm for regular expression matching
Thompson's algorithm for regular expression matching is a classic method that converts regular expressions into nondeterministic finite automata (NFAs) to enable efficient pattern matching in text processing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Viterbi algorithm Target entity description: The Viterbi algorithm is a dynamic programming method used to find the most likely sequence of hidden states in probabilistic models such as Hidden Markov Models, widely applied in fields like digital communications, speech recognition, and bioinformatics.
-
A.
Berlekamp–Massey algorithm
The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
-
B.
Thompson's algorithm
Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
-
C.
Marzullo's algorithm
Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
-
D.
Knuth–Morris–Pratt algorithm
The Knuth–Morris–Pratt algorithm is a classic linear-time string-searching algorithm that efficiently finds occurrences of a pattern within a text by precomputing a prefix function to avoid redundant comparisons.
-
E.
Thompson's algorithm for regular expression matching
Thompson's algorithm for regular expression matching is a classic method that converts regular expressions into nondeterministic finite automata (NFAs) to enable efficient pattern matching in text processing.
- F. None of above. chosen
Statements (61)
| Predicate | Object |
|---|---|
| instanceOf |
algorithm
ⓘ
decoding algorithm ⓘ dynamic programming algorithm ⓘ inference algorithm ⓘ |
| assumes | known model parameters ⓘ |
| basedOn |
Bayesian inference
ⓘ
Markov processes ⓘ
surface form:
Markov property
dynamic programming ⓘ |
| comparedTo |
Baum–Welch algorithm
ⓘ
forward-backward algorithm ⓘ |
| computes |
Viterbi algorithm
self-linksurface differs
ⓘ
surface form:
Viterbi path
most probable path ⓘ |
| field |
bioinformatics
ⓘ
computational biology ⓘ digital communications ⓘ error-correcting codes ⓘ information theory ⓘ machine learning ⓘ natural language processing ⓘ pattern recognition ⓘ signal processing ⓘ speech recognition ⓘ |
| hasStep |
backtracking
ⓘ
initialization ⓘ recursion ⓘ termination ⓘ |
| input | sequence of observations ⓘ |
| maximizes | posterior probability of state sequence ⓘ |
| minimizes | path metric ⓘ |
| namedAfter | Andrew Viterbi ⓘ |
| operatesOn |
Hidden Markov Model
ⓘ
trellis diagram ⓘ |
| originalApplication | decoding convolutional codes in communication systems ⓘ |
| output | most likely sequence of hidden states ⓘ |
| property |
guarantees globally optimal path under model assumptions
ⓘ
uses dynamic programming to avoid recomputation ⓘ works on discrete-time finite-state models ⓘ |
| proposedBy | Andrew Viterbi ⓘ |
| publicationYear | 1967 ⓘ |
| relatedTo |
Bellman–Ford algorithm
ⓘ
Dijkstra ⓘ
surface form:
Dijkstra algorithm
shortest path problem ⓘ |
| spaceComplexity |
O(N T)
ⓘ
O(S T) ⓘ |
| timeComplexity |
O(N^2 T)
ⓘ
O(S^2 T) ⓘ |
| usedFor |
decoding convolutional codes
ⓘ
error correction in digital communication channels ⓘ finding most likely sequence of hidden states ⓘ gene prediction ⓘ hidden state inference in Hidden Markov Models ⓘ maximum a posteriori decoding ⓘ part-of-speech tagging ⓘ profile HMM alignment ⓘ sequence alignment scoring in HMMs ⓘ sequence decoding ⓘ speech recognition decoding ⓘ |
| uses |
backpointers
ⓘ
emission probabilities ⓘ log probabilities ⓘ transition probabilities ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Viterbi algorithm Description of subject: The Viterbi algorithm is a dynamic programming method used to find the most likely sequence of hidden states in probabilistic models such as Hidden Markov Models, widely applied in fields like digital communications, speech recognition, and bioinformatics.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Viterbi path
subject surface form:
Andrew Viterbi
subject surface form:
Andrew Viterbi