Questions tagged [stochastic-processes]
A stochastic process is a collection of random variables usually indexed by a totally ordered set.
2,351
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Multi-type Galton-Watson-like process where only majority-type is allowed to reproduce
Are you aware of any research papers that have explored a multi-type Galton-Watson process in which only particles of the majority type are permitted to reproduce in each generation?
I've been unable ...
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1
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Does stationary increments and self similarity imply Hölder?
Let $X(t)$ be a real valued continuous stochastic process. Suppose that $X(t)-X(s)=(t-s)^a X(1)$ in distribution for some $a\in (0,1)$.
If $X$ has infinitely many moments then Kolmogorov continuity ...
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Existence of Brownian motion using Kolmogorov's extension theorem
When wishing to prove existence of Brownian motion, most authors take the route of defining the desired finite dimensional distributions, showing the family of defined finite dimensional distributions ...
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When are the transition densities of an SDE symmetric?
We fix $T>0$. Let $b:[0, T] \times \mathbb{R}^d \rightarrow \mathbb{R}^d$ and $\sigma:[0, T] \times \mathbb{R}^d \rightarrow \mathcal{M}^\text{sym}_{d \times d}(\mathbb{R})$ be measurable and ...
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A complex question related to a certain convergence of Lévy measures
Consider the sequence of stochastic processes $(X_n, n \geq 1)$, where $X_n = (X_{t;n})_{t\in \mathbb Z}$ and:
\begin{equation}\label{I}\tag{SP}
X_{t;n} = \sum_{j=0}^\infty \theta_{jn} \varepsilon_{t-...
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189
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Stochastic volatility model question
Let suppose that $S_t$ is a process defined as:
$$ \begin{cases}dS_t = \mu S_t\,dt+m(v_t)\,dW^1_t\\ dv_t = \mu_v(v_t)\,dt + \sigma_v(v_t)\,dW^2_t\end{cases}$$
where the two Brownian motions have ...
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Large-deviation inequalities for a class of simple random multivariate polynomials
Let $N$ be a large positive integer and let $[N] := \{1,2,\ldots,N\}$. For any $k$, let $K_{N,k}$ denote the collection of $k$-element subsets of $[N]$. Let $x=(x_1,\ldots,x_N)$ be a uniformly random ...
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Markov process with time varying transition kernels
I cross post this question from StackExchange as it may be more appropriate.
I am interested in studying the evolution of a variable $\alpha_t\in [0,1]$ governed by the following stochastic dynamical ...
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Iteratively merging consecutive symbols of a discrete random process
Consider an i.i.d. source (more generally, an ergodic source) which generates symbols from a distribution $P$ over a finite alphabet $\mathcal{A}$. Consider the following stochastic process: At each ...
2
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Measurability of two hitting times at the stopped $\sigma$-algebra
Let $\mathcal{F}=(\mathcal{F}_t)_{t\ge 0}$ be the complete filtration generated by the Brownian motion $B $, and let $a<0<b$. Define the stopping times
$\tau_a=\inf\{t\ge 0\mid B_t=a\}$ and $\...
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2
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Example of random walk in a random environment (RWRE) saying things on the environment
I was wondering if anyone is aware of works/articles/examples where random walks in a random environment (RWRE) are actually used for obtaining information on the random environment.
To clarify a bit, ...
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Reference request: showing that solution of an Ito SDE stays bounded with positive probability
Assume that we have a (well-posed) Ito SDE of the form $$\mathrm{d} X_t = b(X_t)\,\mathrm{d} t + \sigma(X_t)\,\mathrm{d}W_t \label{1}\tag{1},$$ where $b \colon \mathbb{R}^d \to \mathbb{R}^d$, $\sigma \...
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Prove that $\forall x,y \in \mathbb{R}^d , P_x\{y\in B\mathopen]0,1]\}=0$
I'm folowing the proof of corollary 1.8 page 5 of Mörters - Sample path properties of Brownian motion.
I want to show that $$\forall x,y \in \mathbb{R}^d , P_x\{y\in B\mathopen]0,1]\}=0$$ where $B$ is ...
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Freidlin Wentzell for stochastic differential inclusions
Consider the SDI
$$dX^\varepsilon(t)\in b(X^\varepsilon(t))\,dt + \varepsilon \sigma(X^\varepsilon(t)) \, dB(t).$$
Is there any Freidlin-Wentzell large deviations principle for $X^\varepsilon$?
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Fractional power of a matrix with complex spectrum
I will use the notation I found here https://arxiv.org/abs/1812.01206. Please forgive me if this is a poorly stated question. I'm not sure of the things I wrote in parenthesis.
The paper makes a claim ...
2
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1
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170
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How to show the joint weak convergence?
Given a $T>0$, let $\mathcal{C}[0,T]$ be the space of continuous functions on $[0,T]$. Let $Y_n(t)$ be stochastic processes in $\mathcal{C}[0,T]$. We define the weak convergence in the sense of ...
2
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108
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Controlling the adjoint variables in a stochastically perturbed control problem
Suppose we have a deterministic control problem
$$dX_t = b(X_t, u_t) \, dt$$
on a finite timeframe with no terminal cost; i.e. the objective functional to be maximised is
$$\mathbb E \left [\int_{0}^T ...
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73
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Expected number of steps until a queue of $n$ people has passed all $n$ ordered tests consecutively
We are given a queue of $n$ people $\{p_1, \ldots, p_n\}$. They each have to pass $n$ exams $\{t_1, \ldots, t_n\}$. For simplicity we can "draw" the setting in the following way:
$$[t_n,t_{n-...
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A high probability bound for a Rademacher process
Let $\{x_i(t)\}_{i=1}^n$ be i.i.d. Gaussian processes for $t \in [0, T]$ with
\begin{align*}
\mathbb{E}[x_i(t)] & = 0, \quad \forall i \in [1 : n], \ t \in [0, T] \\
\mathbb{E}[x_i(s) x_i(t)] &...
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Joint tail for Brownian motion $P[B_{t_1}>g_1,...,B_{t_n}>g_n]$
Maybe not surprisingly there seems to be a lack of in-depth study of sharp estimates for the joint tail of Brownian motion over different times
$$P[B_{t_1}>g_1,...,B_{t_n}>g_n]$$
for strictly ...
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450
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Bounding expected maximum via adjacent differences
For $X_i$, $i\in[n]$
be a sequence of integrable random variables.
Is there a universal constant $c>0$ such that
$$\mathbb{E}\max_{i\in[n]}X_i
\le
c\left(
\max_{i\in[n]}\mathbb{E}|X_i|
+
\mathbb{E}\...
1
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1
answer
207
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Rate of convergence to uniform distribution
Let $p=(p(1),\ldots,p(N))$ be a discrete distribution on $[N]:=\{1,2,\ldots,N\}$ with full support (i.e all the $p(i)$'s are strictly positive and sum to $1$). Let $i_1,i_2,\ldots,i_T$ be an iid ...
0
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0
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101
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Laplace transform of a stochastic process
Let $R := (R_1, R_2)$ be a two-dimensional diffusion process defined by the following SDE:
$$\mathrm{d}R_{1,t} = -\lambda_1 R_{1,t} \, \mathrm{d}t + \lambda_1 \sigma(R_{1,t}, R_{2,t}) \, \mathrm{d}W_t$...
1
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Embedded branching random walk converge to some random generalized function?
We know that two dimensional discrete GFF(2d-DGFF) on a box $V_N=N[0,1]^2\cap\mathbb{Z}$ with Dirichlet boundary condition will converge in distribution to the 2d continuum GFF with Dirichlet boundary ...
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References on estimates for suprema of uncentered Gaussian processes?
Let $X_t, t \in T$ denote a centered Gaussian process. Let $d(t, s) = \sqrt{\mathbb{E} (X_t - X_s)^2}$.
Consider a mean function $t \mapsto \mu_t$.
Define the expected supremum
$$
S(T, \mu) = \mathbb{...
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0
answers
97
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Simplify nested sums - on the road of a stochastic problem (mixed replacement / kinda non-replacement)
In short, the expression I want to simplify is the following:
$$ P(l) = \frac{P_0}{n^l} * \left(\sum_{p_l=1}^k p_l \sum_{p_{l-1}=p_l}^k p_{l-1} ... \sum_{p_1=p_2}^k p_1 \right) $$
or eventually easier ...
2
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If $u$ is harmonic, $\exists \alpha,\beta \in \mathbb{R},\forall x\in \mathbb{R}^d,u(x) \leq \alpha |x|+\beta,$ then $u$ is affine
We consider a harmonic function $u:\mathbb{R}^d \to \mathbb{R}$ $(\Delta u=0).$ Suppose that $$\exists \alpha,\beta \in \mathbb{R},\forall x\in \mathbb{R}^d,u(x)\leq \alpha |x|+\beta.$$
Therefore $u-u(...
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1
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Resources to understand Lebesgue measure of Brownian motion's path [closed]
[https://www.math.uchicago.edu/~may/VIGRE/VIGRE2011/REUPapers/Hansen.pdf][page 12] and [peter morters][page 47]
Let $B$ be a stanrd Brownian Motion and $R$ a function defined on $\mathbb{R}^2$ such ...
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answers
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Application of Ito's formula to Liouville's theorem
Liouville's theorem for bounded harmonic functions could be proved using Ito's formula, martingale convergence and Blumenthal's 0-1 law.
I tried checking the classical books on Brownian motion and ...
3
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2
answers
215
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Stability results for general linear stochastic ODE
I am interested in the following time-invariant multivariate SDE:
\begin{equation}
dx_i = \sum_{j} a_{ij} x_j\,dt + \sum_{j,k} b_{ijk} x_k \, dW_j
\end{equation}
Despite its simplicity the general ...
1
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0
answers
71
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Bounding expectation of switching stochastic process
I am analyzing the behavior of an 1D stochastic dynamic system, where the state can vary randomly within a small magnitude. However, when the state deviates too much from zero, its expected magnitude ...
2
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0
answers
115
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Reference book for stochastic processes
I am looking for a good reference book for properties of stochastic processes for applied research. What I would like the reference to have is a collection of results on a large list of stochastic ...
3
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0
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Algebra core for generator of Dirichlet form
This is a question about the existence of a core $C$ for the generator $A$ of a regular Dirichlet form $\mathcal{E}$ having a carré du champ $\Gamma$, so that $C$ is an algebra with respect to ...
2
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1
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Ergodicity of linear dynamical systems and convergence of covariance matrices
Let $z(n+1)=Bz(n)+\xi(n+1)$ be an $N$-dimensional linear dynamical system with $\left(\xi(n)\right)_{n\in\mathbb{N}}$ being i.i.d. with $\xi(n)\sim\mathcal{N}(0,\Sigma_{\xi})$.
Assumptions: a) The ...
3
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1
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Each diffusion SDE is associated to a *unique* family of transition kernels
I consider an SDE of the form $dX_t=b(X_t) \, dt + \sigma(X_t) \, dW_t$, with $b$ and $\sigma$ globally Lipschitz on $\mathbb{R}^n$.
How can I prove that there exists a unique family of transition ...
2
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1
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Are the jumps of a càdlàg function "summable"?
This question is motivated by the question https://math.stackexchange.com/questions/4644235/ on Math Stack Exchange. First, I need to define a notion of transfinite summability that I have not seen ...
3
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1
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Is a semimartingale that is continuous a continuous semimartingale?
Let $X$ be a centered semimartingale that has continuous sample paths almost surely. Is it then true that $X$ is a continuous semimartingale? Meaning that $X$ has a decomposition $X=M+V$ where $M$ is ...
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0
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Locality and restriction properties for self-avoiding and loop-erasing random walks
This question has been cross-posted from math.stackexchange.com : https://math.stackexchange.com/questions/4742746/locality-and-restriction-properties-for-self-avoiding-and-loop-erasing-random-wa
I ...
1
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1
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154
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Is deterministic evolution preserved under weak converge of stochastic processes?
Suppose you have a sequence of continuous stochastic processes $X_N$ with $X_N(0)=0$, and that $X_N$ converge weakly on the space of continuous functions, to a stochastic process $X$. Suppose $X_N$ ...
2
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How much is known about the action functional for small noise diffusions with general volatility coefficients?
Let $W$ be a d-dimensional Brownian motion, and for every $\varepsilon > 0$, let $X^\varepsilon$ be the solution to the SDE
$$dX^\varepsilon_t = b(X^\varepsilon_t) \, dt + \varepsilon \sigma (X^\...
2
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A question related to the jumps of a Levy process
The Lévy–Khintchine formula says that any Lévy process, $X=(X(t), t \geq 0)$, has a specific form for its characteristic function. More precisely, for all $t \geq 0$, $u \in \mathbb R^d$:
$$
\mathbb{E}...
0
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0
answers
50
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Possible covariance matrices of predictions of a stationary process
Let $X_t$ be a discrete time zero-mean real-valued stationary Gaussian process adapted to a $\sigma$-field ${F}_t$. Let us define
$Z_{t,j} \equiv \mathbb{E}[X_{t+j}|{F}_t]$
I am interested in ...
2
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0
answers
37
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Discrete approximation of continuous determinantal point processes
(throughout, "DPP" denotes "Determinantal Point Process")
TL;DR: Discrete DPPs are straightforward to compute with, continuous DPPs less so. Can we approximate continuous DPPs well ...
1
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0
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37
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On a generator of a continuous-time Markov chain
Let $S$ be a countable set with discrete topology and let $X=(\{X_t\}_{t \ge 0},\{P_x\}_{x \in S})$ be a continuous-time Markov chain on $S$. We assume that each $x \in S$ is a exponential holding ...
1
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1
answer
80
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Characteristic exponent after Girsanov transformation
Let $B$ be a standard Brownian motion. Its characteristic exponent (or Fourier transform) is easily calculated to be
$$ \mathbb E [e^{ixB_t}] = e^{-\frac 12 x^2 t}. $$
Now I want to apply a Girsanov ...
1
vote
1
answer
96
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Linear response for SDE
Consider a family of stochastic processes $dX^h_t=(g(X^h_t)+h(s))\,dt+dW_t$ and a functional $I_f:h(s) \rightarrow E[f(X_t^h)] $. I would like to compute the kernel of the derivative of this ...
1
vote
1
answer
61
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Lower bounding the infimum of a random process
Let $X_{t}=\sum_{i=1}^n(1+s\cdot w_i)t_i\sin(t_i)$ where $t\in T=[-\pi/2,\pi/2]^n/\{\vec 0\}$, $w_i$ are iid standard gaussian variables, $s$ is a scalar denoting the strength of Gaussian noise.
How ...
1
vote
2
answers
99
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the infimum of a random process
Let $X_{t}=\sum_{i=1}^n(1+s\cdot w)\sin(t_i)$ where $t\in T=[-\pi/2,\pi/2]^n/\{\vec 0\}$, $w\sim\mathbb{N}(0,1)$, $s$ is a scalar denoting the strength of Gaussian noise. How to find the condition on $...
0
votes
1
answer
114
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Lipschitz maximal inequality for random process
I am confused on the Lemma 5.7 (Lipschitz maximal inequality) here. Let me first restate the definition and the lemma:
Def
$\{X_t\}_{t\in T}$ is called Lipschitz for metric $d$ on $T$ if there exists ...
3
votes
2
answers
816
views
Can independent Brownian motions hit zero at the same time?
Consider for $i=1,\ldots, N\ge2$
$$X^i_t=x_i+W^i_t,\quad \forall t\ge 0,$$
where $x_1,\ldots, x_N\in (0,\infty)$ and $W^1,\ldots, W^N$ are independent Brownian motions. Denote by $\tau_i$ the first ...