A stochastic process satisfying the Markov property: the distribution of the future states given the value of the current state does not depend on the past states. Use this tag for general state space processes (both discrete and continuous times); use (markov-chains) for countable state space ...

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Invariant distributions: Applications in the real World

I'm studying about invariant distributions for Markov processes; say in the context of dynamics of Random Neural Networks (biological Networks). I can't fully understand what does an invariant ...
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Write out the explicit Kolmogorov forward differential equation

Let $(X_t)$ be a continuous-time Markov process with two states, as shown below. Assume that there are two positive numbers $a$ and $b$ such that for all times $t\geq 0$ and $h>0$, $P(X_{t+h} = 2 ...
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Kolmogorov forward and Backwards equation interpretation

Let $\lambda_i$ be the sojourn rate of state i, $q_{ij}$ be the transition rate form i to j, and $p_{ij}$ be the transition probability from i to j. The Kolmogorov Forward and backwards equation are ...
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Simple Random Walk - Why are these two events the same?

Let $S = (S_n)_{n \geq 1}$ be a simple random walk. We denote the hitting time of a point $b$ by $\tau_b = \min \{i \geq 1 : S_i \geq b\}$. My text says that the events $\displaystyle\{\max_{k \leq ...
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Amount of information a hidden state can convey (HMM)

In this paper (Products of Hidden Markov Models, http://www.cs.toronto.edu/~hinton/absps/aistats_2001.pdf), the authors say that: The hidden state of a single HMM can only convey log K bits of ...
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How long does it take two identical hidden Markov models run on same observations to forget their initial distributions (if ever)?

Let $H_1$ and $H_2$ be two instances of a finite Hidden Markov Model (HMM) $H$. That is, $H_1$ and $H_2$ have identical state spaces $Q$ as well as identical transition $A$ and emission probabilities ...
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Simple Hidden Markov Model with Autoregressive Structure - Estimation?

I observe two series over time: $Y_{1:T}=\{ Y_{1}, \dots, Y_{T}\}$ and $X_{1:T}=\{ X_{1}, \dots, X_{T}\}$, where the $X$ series is supposed to be exogenous (I do not define any stochastic process for ...
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311 views

Hidden Markov Model, transition probabilities which are modeled with an exponential distribution

I'm looking at implementing an algorithm described in a paper, but I'm having trouble understanding how the transition probabilities for a Hidden Markov Model are defined. In the first sections, I ...
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9 views

Identification of Infinite Dimensional State in Hidden Markov Model

Consider a hidden markov model (HMM) where the state, $X_t(\alpha)$, is a stochastic distribution over $\alpha \in \mathbb{R}_+$ and one observes a signal $Y_t$, which is simply a moment of this ...
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10 views

emmission probabilities in a hidden markov model with 2 states and an alphabet of 4 characters

I'm reading through a text that is describing how to use use hidden markov models to identify areas of biological sequences that correspond to specific biological features. It starts with a simple ...
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54 views

Hidden Markov Model Transition Probability

I am doing my assignment and I am asked to derive transition probability of a HMM. There are Three states. H, E and T. They initially gave me the information as follow. E is followed by an H 40% of ...
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Binary Hidden Markov Model

Consider a binary HMM with 2 observed variables $O_n \in \{0,1\} \; \forall n \in \mathbb{N}$. Suppose that the hidden Markov process $X_n$ is characterised by a known transition probability matrix ...
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Are queues CTMC?

The $M/M/1$ queue have all the properties of the countable state continuous time markov chain. Is any general queue also a countable state CTMC?
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40 views

How to understand this kind of Markov chain?

There are two unidirectionally coupled processes $X_t$ and $Y_t$. The coupling is $Y_t=g(X_{t-\delta},\dots)$. The Bayesian network of the two process is described in the following figure: Now this ...
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137 views

Bayesian Network vs Markov Decision Process

I am wondering if somebody can tell me anything about the practical differences between using Markov Decision Processes and and Bayesian Networks in reasoning about probabilistic processes?
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Symmetric Simple Random Walk - Definition Clarification

I'm finding conflicting answers everywhere, including in my own notes. In the phrase "symmetric simple random walk", which part, "symmetric" or "simple" refers to having a probability of $0.5$ to go ...
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Markovian Model: scheduling jobs to servers

I have the following problem. I tried to look at queuing theory, but it probably fits better as a scheduling problem. I have a set of $C$ servers: each one can perform 1 job. Processes arrive ...
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Does an embedded discrete-time Markov chain preserve its properties in continuous time?

Given a discrete-time Markov chain without independent increments, is the embedding of it into a continuous time Markov chain (i.e. via the use of exponential waiting times) an example of a continuous ...
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Few Questions on Markov Chains [on hold]

Markov Chain In the picture I have a Markov chain, call it $X(n)$. I have a few questions about this Markov chain. First, is it aperiodic? Second, what is the value of ...
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How to make Markov Chain model from sequence of data in MATLAB?

Markov Chain model considers only 1-step transition probabilities i.e. probability distribution of next state depends only on current state and not on previous state. I have a sequence and from that I ...
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8 views

Predicting Nash equilibrium after one player enters or leaves

Suppose I have a game with $N$ players, and that the Nash equilibrium can be calculated. If one player enters or leaves the game, is it possible to predict or quickly calculate the resulting Nash ...
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Markov Chains that preserve an ordering of the state space

Suppose $X = (X_k)_{k=0}^\infty$ is a homogeneous Markov chain/process (for example on the state space $E = \lbrace 1, \dots, m\rbrace$). We can interpret the elements of the state space as "values". ...
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Dice Game with 1 die and Payoff Function

Imagine a dice game where you may repeatedly roll a die until you either decide to stop, or roll a 1, with the following payoff function (where k is the number on the die), $f(k) = 0$ when $k=1$ ...
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Example of a Continuous-Time Markov Process which does NOT have Independent Increments

1. Given a discrete-time Markov chain without independent increments, is the embedding of it into a continuous time Markov chain (i.e. via the use of exponential waiting times) an example of a ...
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32 views

Markov Process graphical representation

I don't understand how the picture has been constructed. Specifically how $\mu^{11}=-(\mu^{12}+\mu^{13}+\mu^{14})$ and $\mu^{44}=-\mu^{43}$ has been graphically represented. Here $\mu^{ij}$ is the ...
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Markov chain - Stationnary distribution - Unique

Consider the following respective infinitesimal generators of Markov chains in continuous time: \begin{equation} A=\begin{bmatrix} -4 & 1 & 3 \\ 3 & -5 & 2 \\\ 0 & 3 ...
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Show that a matrix satisfying certain conditions is non-singular

I have a square matrix $A$ satisfying the following conditions: The elements on the diagonal are negative; All other elements are non-negative; All row sums are less than or equal to $0$; There is ...
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6 views

What is the probability of this Markov Jump process remaining in this state?

Suppose you had a time homogeneous Markov jump processed defined by the following transition diagram I'm assuming that this means that the process remains in state $0$ for time $t$ with probability ...
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What does the notation $P_{\overline{MM}}(t)$ mean in this context?

The notation $P_{\overline{MM}}(t)$ is used in part (iii) of the following question: I'm unsure of exactly what this notation represents. My guess would be that it represents the probability that a ...
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19 views

Period of an irreducible Markov Chain is given by the number of eigenvalues with unit modulus

Suppose $\{X_n\}$ is an irreducible Markov Chain on finite state space $S$. Then, the number of eigenvalues of the transition matrix with unit modulus is precisely equal to the period of the chain. ...
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Pure jump process

I'm having touble understand the pat of the solution that I have underlined in green for b)
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18 views

Calculating the variance of the time until a Markov process jumps to a specific state from a starting state?

A Markov process on $E = {1, 2}$ is constructed according to holding time parameters $λ_1 = 2$ and $λ_2 = 4$; the defining Markov chain has transition probabilities $p_{11} = p_{12} = 0.5$ and ...
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Deciding whether a maximum asset price process is a markov process

I understand how Mn has been drawn. For the second computing part, after computing, I have no idea how to decide if Mn is a markov process I don't understand the solution at all, don't know what ...
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When can an embedded Markov chain X for a Markov process Y be reducible?

It's pretty widely documented that a Markov process Y is reducible/irreducible if and only if the embedded Markov chain X is reducible/irreducible. However I'm not sure this works in reverse. I'm ...
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How to use the Markov property of Brownian motion

This is a problem from Durrett's probability with examples, exercise 8.2.1. It is not homework. The exercise states: Let $T_0 = \inf\{s > 0 : B_s = 0\}$ and let $R = \inf\{t > 1 : B_t = 0\}$. ...
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451 views

Transition rate matrix from transition probability matrix

If I have a $2 \times 2$ continuous time Markov chain transition probability matrix (generated from a financial time series data), is it possible to get the transition rate matrix from this and if ...
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Expected state of a Markov chain

Let's start with a slightly trivial Markov chain defined as follows: the beginning state is called $1$ and the set of states is $\mathbb{N}$. At each step, when the current state is $n$, the ...
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This is a Markov Chain?

Consider two irreducible ergodic Markov chains with the same state space $\{0, 1, . . . , N\}$, with transition matrices $P$ and $Q$ and respective stationary distributions $\pi$ and $\rho$. We ...
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Poisson: Conditional Probability on Pizza order

I am not sure about my answer. In particular, part b of the following question. Pizza orders arrive according to a Poisson process of rate 20 per hour. Orders are independently for a vegetarian ...
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Distribution of the first exit time of a one-dimensional diffusiom/ Brownian motion

I have a one-dimensional diffusion on $[0,1]$ and I need to calculate the distribution of the first exit time of the interval $(-\epsilon,\epsilon)$ for an $\epsilon > 0$. A good first step would ...
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Prove that $\text{lim}_{\Delta t} \rightarrow 0$ of the transition PDF of a std Weiner process is 0

The transition probability density function of the standard Wiener process is: $$ f(x_2,t_2|x_1,t_1) = \frac{1}{\sqrt{2 \pi (t_2-t_1)}}e^{-\frac{(x_2-x_1)^2}{2(t_2-t_1)^2}} $$ I know that if Markov ...
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three-state Markov chain

a male and a female go to a $2$-table restaurant on the same day. each day the male sits at one or the other of the $2$ tables, starting at the table $1$, with a Markov chain transition matrix: ...
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Question about marked poisson process [migrated]

Let's say I have a Poisson point process on $\left[0,T\right]$ with rate $\lambda\left(t\right)=2t^2$. Suppose I attach a mark $m_t$ to each point $t$ of the process such that $m_t\sim ...
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Random walk on the positive integers with reflecting boundary

Consider a Markov chain $X$ on the positive integers where for each $n$: $$n\longrightarrow 1,\;2,\;3\;\dots \;n,\;n+1$$ with equal probability, and $n\longrightarrow m$ with zero probability if ...
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28 views

Constructing transition graph from transition matrix

Ok so for this question I'm having trouble understanding how the transition graph has been drawn from the given transition matrix. This is what I understand and hopefully someone can correct the ...
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Soft Question - book recommendation - Stochastic Processes

My mother language book on stochastic processes is pretty much complete(~500 pages) but would like another one in English, to have in my library. I'm looking for a similar book containing the ...
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Is it correct to say the expected waiting time is infinite?

Say I have some process that starts in a state in the set of states $S$, each possible starting state having non-zero probability. Some (most) of the starting states eventually result in reaching a ...
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prove homogeneous markov chain

$Y_0, Y_1,Y_2,\dots$ are independent and identically distributed random non-negative integer outcomes. Let X_0 = Y_0 $ Let $X_0 = Y_0$ and $X_n = X_{n-1} - Y_n$ if $X_{n-1}>0$, else $X_n = ...
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Prove that $Y_n=X_{n-1}X_n$ is a markov chain

Let $\{X_n\}_{n=0}^\infty$ a sequence of discrete random variables independent identically distributed. Let $Y_n$ such that $Y_n=X_{n-1}X_n$ for all $n\ge 1$ Is $\{Y_n\}_{n=0}^\infty$ a markov ...
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What is the (practical) difference between a stationary distribution and an equilibrium distribution of a MC?

I know that, for a Markov Chain, a stationary distribution is the (row) vector $\pi$ such that $\pi \cdot P = \pi$, where $P$ is the one-step transition matrix for the MC. Intuitively, I assume that ...