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Performance modeling and evaluation
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At IBM Research - Zurich there is extensive knowledge of and simulation experience
with graphical models, including factor graphs and Bayesan belief
networks.
In particular this means probabilistic inference in graphical models
using the probability propagation approach known as the "belief
propagation algorithm" or the "sum product algorithm".
The applications of such graphical models with sometimes millions
of nodes are
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pattern classification, |
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unsupervised learning, |
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data compression and |
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channel coding. |
The team at IBM Research - Zurich has particularly vast experience with the latter
application.
Deep Computing technology is used for performance evaluation in
terms of probability of error or failure of communication and storage
systems. The IBM Research - Zurich team has particular expertise with Monte Carlo
simulations of such complex systems. These extensive simulations
involve adaptive structures and complex algorithms that must be
validated over extremely long sequences of excitation signals.
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