Difference between revisions of "Expectation-maximization algorithm"
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| + | This method will be to define a set of citation | ||
| + | profiles and then self-consistently assign each case | ||
| + | to the profile it best fits while at the same time adjusting | ||
| + | the shape of the profiles to best fit the cases assigned to | ||
| + | them. The means by which they accomplish this task is the | ||
| + | expectation-maximization (EM) algorithm. | ||
| + | |||
| + | |||
| + | The EM algorithm is an established tool of statistics, | ||
| + | but one that is relatively new to network analysis. This paper describe a different | ||
| + | application to the analysis of the temporal profiles of | ||
| + | citations. | ||
| + | |||
| + | |||
| + | In essence the EM algorithm is a method for fitting a | ||
| + | model to observed data by likelihood maximization, but | ||
| + | differs from the maximum likelihood methods most often | ||
| + | encountered in the physics literature in that it does not | ||
| + | rely upon Markov chain Monte Carlo sampling of model | ||
| + | parameters. Instead, by judicious use of “hidden” variables, | ||
| + | the maximization is performed analytically, resulting | ||
| + | in a self-consistent solution for the best-fit parameters | ||
| + | that can be evaluated using a relatively simple iteration | ||
| + | scheme. | ||
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| + | |||
| + | |||
| + | |||
| + | |||
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| + | == Reference == | ||
| + | |||
[http://en.wikipedia.org/wiki/Expectation-maximization_algorithm Expectation-maximization algorith on Wikipedia]. | [http://en.wikipedia.org/wiki/Expectation-maximization_algorithm Expectation-maximization algorith on Wikipedia]. | ||
| − | [http://www.ebook3000.com/The-EM-Algorithm-and-Extensions_4742.html G.J. McLachlan, T. Krishnan, The EM Algorithm and Extensions | + | [http://www.ebook3000.com/The-EM-Algorithm-and-Extensions_4742.html G.J. McLachlan, T. Krishnan, The EM Algorithm and Extensions]. (Warning: this is an e version of this excellent book, I put it here just for introduction purpose, if you like this book, please borrow it in a library or buy it) |
| − | (Warning: this is an e version of this excellent book, I put it here just for introduction purpose, if you like this book, please borrow it in a library or buy it) | ||
Latest revision as of 00:56, 7 February 2011
This method will be to define a set of citation profiles and then self-consistently assign each case to the profile it best fits while at the same time adjusting the shape of the profiles to best fit the cases assigned to them. The means by which they accomplish this task is the expectation-maximization (EM) algorithm.
The EM algorithm is an established tool of statistics,
but one that is relatively new to network analysis. This paper describe a different
application to the analysis of the temporal profiles of
citations.
In essence the EM algorithm is a method for fitting a
model to observed data by likelihood maximization, but
differs from the maximum likelihood methods most often
encountered in the physics literature in that it does not
rely upon Markov chain Monte Carlo sampling of model
parameters. Instead, by judicious use of “hidden” variables,
the maximization is performed analytically, resulting
in a self-consistent solution for the best-fit parameters
that can be evaluated using a relatively simple iteration
scheme.
Reference
Expectation-maximization algorith on Wikipedia.
G.J. McLachlan, T. Krishnan, The EM Algorithm and Extensions. (Warning: this is an e version of this excellent book, I put it here just for introduction purpose, if you like this book, please borrow it in a library or buy it)
