Statistical estimation of ergodic Markov chain kernel over discrete state space

Ergodic Theory for Countable State Space Markov Chains

Here we want to see to what extent the results from Doeblin’s theory for finite state spaces extend to the case when the state space is countably infinite. Throughout we will use π to denote the row vector whose jth component is πj = ( E[ρj |X0 = j] )−1. Theorem (A). If j is transient, then πj = 0 = limn→∞(P)ij for all i. Proof. Because P(ρj = ∞|X0 = j) > 0, it is clear that πj = 0. Thus, all t...

AN APPLICATION OF TRAJECTORIES AMBIGUITY IN TWO-STATE MARKOV CHAIN

In this paper, the ambiguity of nite state irreducible Markov chain trajectories is reminded and is obtained for two state Markov chain. I give an applicable example of this concept in President election

Statistical analysis of neural data: Discrete-space hidden Markov models

analysis of ruin probability for insurance companies using markov chain

در این پایان نامه نشان داده ایم که چگونه می توان مدل ریسک بیمه ای اسپیرر اندرسون را به کمک زنجیره های مارکوف تعریف کرد. سپس به کمک روش های آنالیز ماتریسی احتمال برشکستگی ، میزان مازاد در هنگام برشکستگی و میزان کسری بودجه در زمان وقوع برشکستگی را محاسبه کرده ایم. هدف ما در این پایان نامه بسیار محاسباتی و کاربردی تر از روش های است که در گذشته برای محاسبه این احتمال ارائه شده است. در ابتدا ما نشا...

Markov chain Monte Carlo for continuous-time discrete-state systems

A variety of phenomena are best described using dynamical models which operate on a discrete state space and in continuous time. Examples include Markov (and semiMarkov) jump processes, continuous-time Bayesian networks, renewal processes and other point processes. These continuous-time, discrete-state models are ideal building blocks for Bayesian models in fields such as systems biology, genet...