#markov model

[machine-unlearning]

You know what, screw you *unhides your Markov model*

[altanai]

Markov model

In probability theory, a Markov model is a stochastic model used to model randomly changing systems.

Stochastic process

Let’s see what Stochastic process is first .

Stochastic is, having a random probability distribution or pattern that may be analysed statistically but may not be predicted precisely., or in simple terms refers to a randomly determined process . In probability theory and related…

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[jonathankinlayqr-blog]

Regime Switching

Regime Switching might be an approach to Pairs Trading. A 2-state Markov Model can explain up to 65% variations and non-normality in the returns process.

[scirp]

Read the full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49676 DOI: 10.4236/ajor.2014.45029 Author(s) Nguyen Khac Minh, Nguyen Khac Minh, Pham Van Khanh Affiliation(s) National Economics University, Hanoi, Vietnam. National Economics University, Hanoi, Vietnam. Institute of Economics and Corporate Group, Hanoi, Vietnam. ABSTRACT This study develops the approach by Minh & Khanh [1] to the classic Barro and Sala-i-Martin method [2], [3] named “expanded Barro regression method”, and applies this approach in analyzing the convergence of provincial per capita GDP in Vietnam over the period of 1991-2007. Different aspects of provincial convergence are considered in this paper. The estimated result on conver-gence from our model is compared to other models. gjreww140917 KEYWORDS Convergence, Barro Regression, Markov Model, Expanded Barro Regression

[diggdata]

State transition probabilities of a Markov model

When we are building markov model often we need to calculate state transition probabilities for 2 or 1 history steps. The following function can help you calculate these probabilities in R.

https://gist.github.com/ankitksharma/b6a63c5dfb9cbb5f27c5

The following code will explain how to find the probabilities for 2 history steps:

history <- 2 data <- as.factor(c(1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,1,1)) levels(data)[1] <- "NoFault" levels(data)[2] <- "Fault" data [1] Fault Fault Fault Fault NoFault NoFault NoFault NoFault NoFault NoFault NoFault NoFault NoFault Fault Fault [16] Fault NoFault NoFault NoFault Fault Fault Fault

So, here we are trying to find the probability of getting a Fault state in next time step if for t-1 and t-2 steps the system had NoFault and NoFault states resp.

probabilities <- markovStateTransition(history, data) probabilities t2 t1 t probability 1 NoFault NoFault NoFault 0.8000000 2 Fault NoFault NoFault 1.0000000 3 NoFault Fault NoFault 0.0000000 4 Fault Fault NoFault 0.3333333 5 NoFault NoFault Fault 0.2000000 6 Fault NoFault Fault 0.0000000 7 NoFault Fault Fault 1.0000000 8 Fault Fault Fault 0.6666667

Therefore from the above table we can say the Probability of Fault at t given at t-1 & t-2 the state was NoFault and NoFault respectively is 0.2 (row 5)

NOTE: Till now I have implemented this for only 2 discrete state and future work will be to come up with a general function for n different states.

[samonastra]

1:00 -- Metapopulation Modeling and Analysis with Demographic Stochasticity

*Metapopulation is defined as groups collect through migration *Demographic Stochasticity is defined as the natural variability of birth, death, migration per year, even given constant overall probability *Transition matrix *Markov model (http://en.wikipedia.org/wiki/Markov_model  ...  http://www.ncbi.nlm.nih.gov/pubmed/8246705) *Probability distribution, matrices *Difference equation model