MarkovChains
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(Mucho addenda.) |
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A term from the study of Probability. |
A term from the study of Probability. |
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| − | (Named after [[AndreiMarkov]]) A model of sequences of events where the probability of an event occurring depends upon the fact that a preceding event occurred. |
+ | (Named after AndreiMarkov) A model of sequences of events where the probability of an event occurring depends upon the fact that a preceding event occurred. |
This enables both learning of patterns, and "replays" of existing and novel patterns. Note that the pattern probably has to be linear, but that's about it. |
This enables both learning of patterns, and "replays" of existing and novel patterns. Note that the pattern probably has to be linear, but that's about it. |
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and generate a sentence beginning "Roses", then we have two choices for the second word. If the chainer chooses "are", we proceed as above. If the chainer chooses "feed", the only possible next word is "aphids." |
and generate a sentence beginning "Roses", then we have two choices for the second word. If the chainer chooses "are", we proceed as above. If the chainer chooses "feed", the only possible next word is "aphids." |
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| − | Additional and more complex statements can be generated with a larger initial corpus. As an exercise for the reader, consider what would happen if a Markov chainer were fed a long series of definitions of the form "''x'' is ''y''", and Gertrude Stein's famous edict that "A rose is a rose is a rose". |
+ | Additional and more complex statements can be generated with a larger initial corpus. As an excercise for the reader, consider what would happen if a Markov chainer were fed a long series of definitions of the form "''x'' is ''y''", and Gertrude Stein's famous edict that "A rose is a rose is a rose". |
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| − | An implementation of these ideas might one be the [[IRCBot]] [[MB-01]]. |
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Revision as of 12:34, 13 April 2002
A term from the study of Probability.
(Named after AndreiMarkov) A model of sequences of events where the probability of an event occurring depends upon the fact that a preceding event occurred.
This enables both learning of patterns, and "replays" of existing and novel patterns. Note that the pattern probably has to be linear, but that's about it.
A predominant use of Markov Chains is learning and replaying English grammar, so I'll take my example from that domain:
Imagine two sentences, fed to a Markov chainer:
Roses are red. Violets are blue.
If you ask the Markov chainer to play back a sentence beginning with "Roses", the first word will obviously be "Roses". In the two sentences above, there is only one word that ever is used after "Roses", which is "are". However, in the example sentences, there are *two* possible ways to follow on from "are" - "red", and "blue" - therefore the Markov chainer will pick one at random.
If we add a third sentence, say:
Roses feed aphids.
and generate a sentence beginning "Roses", then we have two choices for the second word. If the chainer chooses "are", we proceed as above. If the chainer chooses "feed", the only possible next word is "aphids."
Additional and more complex statements can be generated with a larger initial corpus. As an excercise for the reader, consider what would happen if a Markov chainer were fed a long series of definitions of the form "x is y", and Gertrude Stein's famous edict that "A rose is a rose is a rose".
