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It has the correct scaling behaviour too, which stochastic / markovian models lack.
The ﬁ rst one, mostly used in the ﬁ eld of speech recognition, models natural language as a markovian stochastic process in which each charac - ter, word or text token is considered as a random variable with a certain probability distribution.
This section focuses on markovian language models based on frequen - cies, since these are the most commonly used models in the ﬁ eld of AAC communication, and the models that have been employed in this thesis.
The reduced degrees of freedom are modelled in terms of first-order markovian Åred noise’ correlation structure of the data series, described by the lag-one autocorrelation coefficient à?
In fact, if the markovian model proved accurate (and I’ll bet it would:-) one could use the network recursively with simulated inputs drawn from the expected distribution of e.g. sunspots and get an actual prediction of global future temperatures some years in the future that might even accurately describe the hidden feedback stabilization mechanisms that are obviously absent from quasi-linear (monotonic and non-delayed) models.
For example, it would be interesting to try training a NN to predict “this year’s mean temperature” from (say) the last twenty-five years’ sunspot data, the last twenty-five years global temperature data (to give it a mechanism for doing the integro-differential non-markovian part of the solution) and with an input for some measure of the total volcanic activity for the last twenty five years.