We tested scenarios with an estimate of relative treatment benefit from meta-analysis, alternate utility values, alternate distributions for survival extrapolation and we explored structural model assumptions using a

Markov model.

In addition we built a first order

Markov model to project trends in conversion of gravel rooftops to other materials in the eastern study region.

The main innovation comes from the fact that we introduced a Hidden

Markov model (HMM) to capture structural breaks in the dynamics of volatility, and thus, trying to improve the investment decisions.

In order to illustrate how the

Markov model equations are developed, assume we have an example illustrated by figure 2.

In Section 3, optimal threshold for Bayesian energy detection (BED) algorithm is re-derived, the benefit of

Markov model is analyzed, and the MBED algorithm is proposed.

Two Lithuanian recognizers using the word based and phoneme-based hidden

Markov models (HMM) were prepared for the implementation of the hybrid recognizer.

Experiments were carried out for the ergodic and left-right Hidden

Markov Models. According to experiments ergodic HMMs with 2 states show best performance.

Thus, the cumulative sum of the product of the traffic flow average value and its corresponding probability from the current state to other states can be taken as the predicted value of the grey

Markov model:

Hidden

Markov Model (HMM) is a probabilistic sequence classifier which could give a probabilistic prediction of driving state over future based on past driving behavior.

State sequence for the Hidden

Markov Model (HMM) is invisible but we can track the most likelihood state sequence based on the model parameter and a given observational sequence.

Another study by Hardy (2001) tried comparing the lognormal model, autoregressive model, autoregressive conditional heteroskedastic model and Markov switching models to derive the distribution function of stock volatility and found that a two-stage

Markov model appears to be the best fit out of all alternative models.

This paper uses a two-regime model to understand more about crash risk by assessing uncovered interest parity (UIP) deviations in a range of CEE countries and by using a hidden

Markov model (HMM) to divide the deviations into two categories: those where the high-yield currency appreciates against the lower interest rate unit (adding a capital gain to the funding premium) and those where the high-yield currency falls much more than would be anticipated by UIP.