Edit distance, also known as Levenshtein distance, is a way to quantify how dissimilar two strings (e.g., words) are to one another by counting the minimum number of operations required to transform one string into the other. It is a widely used string metric in computer science, particularly in natural language processing and bioinformatics.

The three types of operations typically considered in the calculation of edit distance are:

1. Insertion: Adding a character to the string.
2. Deletion: Removing a character from the string.
3. Substitution: Replacing one character with another.

For example, let's consider the edit distance between the words "kitten" and "sitting":

1. kitten → sitten (substitution of 'k' with 's')
2. sitten → sittin (substitution of 'e' with 'i')
3. sittin → sitting (insertion of 'g' at the end)

The edit distance between "kitten" and "sitting" is 3 because a minimum of three operations is required to transform "kitten" into "sitting".

The edit distance can be calculated using dynamic programming. The algorithm typically involves creating a matrix where the cell at position (i, j) represents the edit distance between the substrings of the two words up to positions i and j, respectively. The matrix is filled in iteratively, considering the possible operations at each step.

Applications of edit distance include:

1. Spell checkers: Suggesting corrections for misspelled words.
2. Text search: Finding approximate matches for search queries.
3. Plagiarism detection: Measuring the similarity between documents.
4. Bioinformatics: Comparing DNA or protein sequences.
5. Machine translation: Evaluating the quality of translated text.

There are variations of edit distance that assign different costs to the various operations (insertion, deletion, substitution) or consider additional operations like transposition (swapping adjacent characters).