2010, Vol.13, No.3, pp.267-276
In this paper we give a geometrical interpretation of
regime-switching models when the changing mechanism between the
states is governed by an unobservable Markov process. In
particular we consider a stochastic two-regimes model of price
behavior with time-varying parameters and we prove that the space
of the conditional probability distributions is an exponential
family where the information at the previous time is an hidden
variable. Analogously to the neural case, we obtain a network
trained by various input signals and corresponding output
behaviors. This mechanism has been remarked as the universal way
for the transfer of the information. We also deduce that in this
case (EM) and (em) Algorithms are equivalent.
Key words:
regime-switching models, information geometry, neural networks,
(em) and (em) algorithms
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