The Markov Memory for Generating Rare Events

Financial Risk Modeling with Markova Chain

Investors use different approaches to select optimal portfolio. so, Optimal investment choices according to return can be interpreted in different models. The traditional approach to allocate portfolio selection called a mean - variance explains. Another approach is Markov chain. Markov chain is a random process without memory. This means that the conditional probability distribution of the nex...

Minimum memory for generating rare events.

We classify the rare events of structured, memoryful stochastic processes and use this to analyze sequential and parallel generators for these events. Given a stochastic process, we introduce a method to construct a process whose typical realizations are a given process' rare events. This leads to an expression for the minimum memory required to generate rare events. We then show that the recen...

Modeling Gasoline Consumption Behaviors in Iran Based on Long Memory and Regime Change

In this study, for the first time, we model gasoline consumption behavior in Iran using the long-term memory model of the autoregressive fractionally integrated moving average and non-linear Markov-Switching regime change model. Initially, the long-term memory feature of the ARFIMA model is investigated using the data from 1927 to 2017. The results indicate that the time series studied has a lo...

The KTH Visit in Semi-Markov Processes

An Adaptive Approach to Increase Accuracy of Forward Algorithm for Solving Evaluation Problems on Unstable Statistical Data Set

Nowadays, Hidden Markov models are extensively utilized for modeling stochastic processes. These models help researchers establish and implement the desired theoretical foundations using Markov algorithms such as Forward one. however, Using Stability hypothesis and the mean statistic for determining the values of Markov functions on unstable statistical data set has led to a significant reducti...