Viterbialgoritmi

Viterbi algorithms are a class of dynamic programming algorithms for finding the most likely sequence of hidden states, called the Viterbi path, that results in a sequence of observed events. These algorithms are widely used in fields such as speech recognition, telecommunications, and bioinformatics. The core idea behind the Viterbi algorithm is to use dynamic programming to efficiently compute the most probable path through a state machine or hidden Markov model (HMM). At each step, it considers the possible transitions from the previous state and selects the one that maximizes the probability of reaching the current state given the observed data.

The algorithm iteratively calculates the probability of the most likely path ending in each possible state at each time step. This is done by keeping track of two values for each state at each time step: the maximum probability of reaching that state and the predecessor state that led to that maximum probability. By storing these predecessor pointers, the algorithm can reconstruct the most likely path once it has processed all the observed data. This process avoids the exponential complexity of trying all possible paths.

The Viterbi algorithm is particularly useful when dealing with sequences where the underlying states are not directly observable but can be inferred from a sequence of observations. Its efficiency and effectiveness have made it a cornerstone in many signal processing and machine learning applications.