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2 votes
1 answer
387 views

Weak convergence of sum of log normal random variables

Let $S_t$ be the Geometric Brownian Motion, we know that $$dS_t=rS_tdt+\sigma S_tdW_t, t\in [0,T], S_0>0, r>0,\sigma>0$$ and the distribution of $S_t$ is known explicitly. Please see the ...
KNN's user avatar
  • 323
2 votes
1 answer
421 views

Extending Wald's equation to two classes of i.d. random variables?

I try to adopt Wald's equation to a slightly more complex problem. In fact, after a full day, I found some solution now, but it has a confusing argument in the middle. Perhaps somebody can help me at ...
cubic lettuce's user avatar
2 votes
1 answer
447 views

MCMC with progressive demollification of delta distributions

Edit: I simplified the example to a canonical case for clarity. Given an integral $\int_{\Omega}{g(\mathbf{x})}$ with a well-posed integrand $g(\mathbf{x})$ defined on some multidimensional space $\...
2 votes
0 answers
84 views

Concentration result for self-normalized empirical process

In Theorem 1.1 of this paper by Bercu, Gassiat and Rio, a concentration result is derived for the 'self-normalized' empirical process. Specifically, suppose that $(X,X_n)_{n \ge 1}$ is a sequence of i....
WeakLearner's user avatar
2 votes
0 answers
124 views

Rough path expected signature vs cumulant-generating function / characteristic function

What is the point of using rough path expected signature to characterize the law of а stochastic process when the cumulant generating function is known ($\log\mathbb{E}[e^{i\theta X(t)}]$)? Since an ...
anatolvitold's user avatar
2 votes
0 answers
109 views

Tightness of Hilbert-space-valued arrays

Let $\mathcal{H}$ be a separable Hilbert space. Assume we have some triangular array $W_{n,j}, j=1, \ldots ,n $ of $\mathcal{H}$-valued random elements with $\mathbb{E} \Vert W_{n,j} \Vert_{\mathcal{H}...
esner1994's user avatar
2 votes
0 answers
173 views

Weak convergence of $\mathcal{L}^2$ valued random variables

Consider two continuous functions $f,g: \mathbb{R}^{2} \rightarrow \mathbb{R}$ with $f(x,\cdot), g(x,\cdot) \in \mathcal{L}^2(\mathbb{R},\mathcal{B},\lambda)$ for all $x \in \mathbb{R}$ and a sequence ...
esner1994's user avatar
2 votes
0 answers
169 views

Stochastic Approximation in Reproducing Kernel Hilbert Space

Consider an iterative algorithm with incremental updates \begin{align} x_{t+1} = x_t + \alpha_t \cdot [ h(x_t) + M_{t+1}], \end{align} where $\{x_t \}_{t \geq 0}$ is in a reproducing kernel Hilbert ...
Steve's user avatar
  • 1,127
2 votes
1 answer
658 views

Extension of Talagrand contraction lemma (on empirical Rademacher complexity)

Is the following true? Let $(x_1,...,x_N)$ be a set of points on the unit sphere $S^{d-1}$. Let $\ell_x: [-1,1]\rightarrow [0,1]$ be a family of Lipschitz functions indexed by $x\in S^{d-1}$, with ...
axk's user avatar
  • 517
2 votes
0 answers
101 views

Best describing a stochastic process in terms of others

Intuitive Question Suppose I'm given a set of $k$ time-series $\{X_t^1,\dots X_t^k\}$. Is there a way to determine how much of each series is dependent on the others. Formal Question More ...
ABIM's user avatar
  • 5,405
2 votes
0 answers
87 views

A question about probabilistic graphical models

Say one is given a probabilistic graphical model and a cut of the underlying graph. Do we know any statements about when and how can one or many of the marginals (of the sources) or the conditionals (...
gradstudent's user avatar
  • 2,246
2 votes
0 answers
619 views

Laplace transform of a integral function of CIR/CEV process

The Cox–Ingersoll–Ross model (or CIR model) describes the evolution of interest rates. Constant elasticity of variance model (CEV) is a stochastic volatility model, which attempts to capture ...
KNN's user avatar
  • 323
2 votes
0 answers
1k views

Moments of function of Poisson process

(I'm new to Poisson processes, so please edit if my terminology is incorrect.) Edit: per comments, here is a (more) general version of the originally posted problem (which is now at the bottom, below ...
David M Kaplan's user avatar
1 vote
1 answer
385 views

How fast does this Gaussian random walk move away from the origin?

Suppose $z_i$ are IID zero-centered $d$-dimensional Gaussian random variables with unit-trace covariance $\Sigma$ and $g(z_i)$ is the sum of its components. Consider the following random walk: $$x_s=\...
Yaroslav Bulatov's user avatar
1 vote
2 answers
175 views

is there an interpretation to the inverse of $I-M$ in multitype branching process, where $M$ is the mean matrix?

Assume we have a multitype branching process, i.e., we have a mean matrix $M_{ij}$ and $M_{ij}$ is the expected count of generating $j$ from $i$ in one time step, i.e.: $M_{ij} = \sum_{r} n(r,j)P(r | ...
rolling stone's user avatar
1 vote
2 answers
277 views

Distribution of interarrival times for a special class of stochastic point processes

I am interested in Poisson-binomial stationary point processes (here on the real line) defined as follows. Let $t_k=k/\lambda$, with $k\in\mathbb{Z}$ and $\lambda>0$, $F_s(x)$ be a symmetric, ...
Vincent Granville's user avatar
1 vote
1 answer
259 views

Test for OU-Process

Suppose that I'm given a sample from time-series $(x_n)_{n=1}^N$ and want to decide if it comes from an OU process or not. Is there a (rigorous) test I can use? So far, everything I've seen is hand-...
ABIM's user avatar
  • 5,405
1 vote
2 answers
772 views

Gibbs sampling step size

I have some data generated using MCMC methods and in particular Gibbs sampling. I computed the autocorrelation but I'm unsure how to determine how many samples to skip. I'd like to determine that ...
s5s's user avatar
  • 87
1 vote
1 answer
229 views

Gaussian width of intersection of cube and ball in high-dimensional euclidean space

Let $d$ be a large positive integer and fix $r \ge 0$. Set $S := B_2^n \cap [-r,r]^d$, where $B_2^d$ is the euclidean unit-ball in $\mathbb R^d$. Finally, let $\omega(S)$ be the Gaussian width of $S$, ...
dohmatob's user avatar
  • 6,853
1 vote
1 answer
410 views

Occupation times for two-state Markov processes

Consider a two-state Markov process in continuous time, with states labelled $A$ and $B$. The transition rates for going from state $A$ to $B$, and state $B$ to $A$ are $\alpha$ and $\beta$ ...
StatisticalMechanic's user avatar
1 vote
1 answer
519 views

The integral of a Gaussian process on a unit sphere

Suppose there exist a zero-mean Gaussian process $\mathbb{G} f_u$ indexed by $u \in \mathcal{S}^{p - 1}$ with known covariance $\mathrm{E} \big[ \mathbb{G} f_u \mathbb{G} f_v \big]$ when both $u$ and $...
香结丁's user avatar
  • 331
1 vote
2 answers
302 views

how to derive stationary distribution of maximal entropy random walk

I was reading the paper 0810.4113v2, burda, which analyzed the stationary distribution maximal entropy random walk on the irregular lattice. I am confused on some of the steps. Description: The ...
Nick Dong's user avatar
  • 211
1 vote
1 answer
207 views

An efficient method to find the MLE of the combination of two point processes

I have a point process defined in two parts as follows. Consider first the main process which we call $A$ which is homogeneous Poisson process with conditional intensity $$\lambda(t) = \mu$$ For ...
Simd's user avatar
  • 3,377
1 vote
1 answer
294 views

Kalman Filter...Denoising measurement data to track objects

Hi Everyone, I am about to implement a Kalman Filter in a software. I found this very helpful article here: http://bilgin.esme.org/BitsBytes/KalmanFilterforDummies.aspx The example helps a lot, ...
user27298's user avatar
1 vote
1 answer
93 views

An inequality relating $\ell_1$ distance of input and output of a Markov krnel

Let $K$ be a Markov kernel from $\mathcal{X}$ to $\mathcal{Y}$, i.e., $K(\cdot|x)$ is a probability measure on $\mathcal{Y}$ for all $x\in \mathcal{X}$. Let $\mu$ and $\nu$ be two probability measures ...
math-Student's user avatar
  • 1,109
1 vote
1 answer
199 views

Rademacher complexity for a family of bounded, nondecreasing functions?

Let $\{\phi_k\}_{k=1}^K$ be a family of functions mapping from an interval $[a, b]$ to $[-1, 1]$. That is, $\phi_k \colon[ a,b] \to [-1, 1]$ are nondecreasing maps on some finite interval $[a, b] \...
Drew Brady's user avatar
1 vote
1 answer
216 views

Rademacher complexity of function class $(x,y) \mapsto 1[|yf(x)-\alpha| \ge \beta]$ in terms of $\alpha$, $\beta$, and Rademacher complexity of $F$

Let $X$ be a measurable space and let $P$ be a probability distribution on $X \times \{\pm 1\}$. Let $F$ be a function class on $X$, i.e., a collection of (measurable) functions from $X$ to $\mathbb R$...
dohmatob's user avatar
  • 6,853
1 vote
1 answer
221 views

Large deviation for empirical median

I found this exercise while reading some notes on Large Deviation Principle. This exercise is at the end of the very first chapter, including Cramer's Theorem and essentially nothing more (no Sanov ...
rime's user avatar
  • 445
1 vote
1 answer
141 views

Central limit theorem for chi-squared random field on $\mathbb R^p$

Let $X:x \mapsto X(x)$ be a centered stationary Gaussian process on the $\Omega:=\mathbb R^p$, such that $X(x) \overset{d}{=}X(x')$ for all $x,x' \in \Omega$. Set $\sigma^2 := \mbox{Var}(X(0)) = \...
dohmatob's user avatar
  • 6,853
1 vote
1 answer
375 views

convergence of Bayesian posterior with non iid data

Let $(\epsilon_t)_t$ be a sequence of iid random variables, distributed according to the density $f:\mathbb{R}\to (0,\infty)$ and $$ x_t = q( \theta^\star, x_1,x_2, \ldots, x_{t-1}) + \epsilon_t \,. ...
Peter's user avatar
  • 355
1 vote
1 answer
124 views

"Convergence speed" results for the Langevin process

The Langevin process is defined by the following stochastic differential equation: $$ \dot X = - \nabla \phi + \sqrt 2 dW_t $$ Its equilibrium distribution is the following: $$ p_\infty (x) \propto ...
Guillaume Dehaene's user avatar
1 vote
1 answer
142 views

Subclass of semimartingales for which all characteristics can be estimated?

I'm going to ask the question for Ito semimartingales rather than semimartingales in general, but more general answers would be great. An Ito semimartingale is a martingale for which the ...
Michael's user avatar
  • 273
1 vote
0 answers
53 views

The limit ratio of two Markov Chain Probability

Suppose there are two given SDE in $\mathbb{R}^d$: $$ \begin{align} \left\{ \begin{aligned} dX_t&=\begin{bmatrix}-\nabla V(X_t)+2\beta^{-1}v_F^\theta(X_t)\end{bmatrix}dt+\sqrt{2\beta^{-1}}dW_t,&...
Francis Fan's user avatar
1 vote
0 answers
112 views

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)] &...
fs l's user avatar
  • 81
1 vote
0 answers
39 views

Characteristic function of a Dirichlet Process

Suppose $P \sim \text{DP}(\alpha,G) $ where $G \sim N(0,1)$ is the base measure and $\alpha > 0$ is the concentration parameter. The stick breaking representation says that $P$ can be expressed as \...
Zilch's user avatar
  • 11
1 vote
0 answers
83 views

Properties of max of many linear combinations of a multivariate normal vector and/or sum of top $k$ elements of a multivariate normal vector

Thank you in advance for your help! I am interested in studying the following probability: $$P\big[\max_{H \subset X,|H|=k} \sum_{i \in H} \mathbf{a}_i^T \mathbf{w} \ge 0 \big],$$ where $\mathbf{a}_i$ ...
Vergil's user avatar
  • 11
1 vote
0 answers
68 views

(Anti-)concentration of gap between largest and second largest component of multivariate random gaussian vector

Let $n$ be a large positive integer and let $Y=(Y_1,\ldots,Y_n)$ be a zero-centered random $n$-dmensional real vector with covariance matrix $\Sigma$, an $n$-by-$n$ positive definite matrix with ...
dohmatob's user avatar
  • 6,853
1 vote
0 answers
49 views

semi-parametric regression

Suppose the observation $(X_1, Y_1), \ldots, (X_n, Y_n)$ satisfies the following semi-parametric model $$Y_t = m(X_t, \alpha) + \sigma(X_t, \beta) U_t,$$ where $U_t$ is independent with $X_t$ with ...
香结丁's user avatar
  • 331
1 vote
0 answers
99 views

Covering number after projection

In these lecture notes on Statistical Learning Theory we find the following definitions for covering numbers: Definition. Let $(\mathcal{W}, d)$ be a metric space and $\mathcal{F} \subset \mathcal{W}$...
Jonas Metzger's user avatar
1 vote
1 answer
170 views

Stationary distribution of Markov Chain with departure

I have a Markov Chain of $N$ states. Such states represent the energy levels in a molecule. The states' connectivity is as follows: States $j\in\{0,\ldots,N\}$ transition to $k\in\{\max(j-M,0),...,\...
TheVal's user avatar
  • 151
1 vote
0 answers
61 views

For 1-NN (Next Neighbor), what is the expectation of the largest probability of being the nearest neighbor?

Suppose we sample $n$ points $X_1,X_2,...,X_n$ independently from a distribution $P_X$ on $[0,1]^d$. For a new point $X$ independently from $P_X$, we find its nearest neighbor in $X_1,X_2,...,X_n$. ...
Yichong Xu's user avatar
1 vote
1 answer
259 views

Conditional Expectation Relative to "Random Time" - Consistency of the Substitution Rule

I am thinking of the following situation: On a probability space $\left( \Omega, \mathscr{F}, \cal{P} \right)$ with arbitrary structure, suppose we are given a random function (as it is called in the ...
underpi's user avatar
  • 75
1 vote
0 answers
44 views

Validating a probability density distribution forecast model for a Markov process

Let's say we have a Markov process $X_t$, and we come up with a forecast model that takes some information from outside world and says: "value $X_{t+1}$ has probability density distribution $P_t(x)$". ...
mt_christo's user avatar
1 vote
0 answers
101 views

How to fit a stochastic matrix to given data.?

Given a data sequence of noisy observations of a 3-state Markov chain $X$ -- $y_1$,$y_2$,...$y_n$, with two transition matrices $A_1$ and $A_2$ corresponding to different regions (**) in the (unit) ...
inherited_knowledge's user avatar
1 vote
0 answers
251 views

Inflated independent samples for Monte Carlo estimation

In my particular problem, running an MCMC is too expensive, so I'm looking for a simple MC estimator, which would partially inherit the correlated samples of MCMC, yet would not require computing ...
Anton's user avatar
  • 101
1 vote
0 answers
132 views

Eigen value distribution of autocorrelated Wishart matrix

Suppose the matrix W is constructed as $W=XX^T$ where $X_i(t) = \phi_i X_i(t-1) + a_i(t)$, and $a_i(t)$ ~ $N(0,1)$. I am interested in knowing the eigen value distribution of W. My google search on ...
Sinbaski's user avatar
0 votes
2 answers
261 views

What mathematical formalism might be used to disprove natural selection, on the basis that there are too many independent genetic parameters? [closed]

I have nagging doubts that the random genetic mutation process of natural selection is sufficient to explain evolution, even when coupled with sexual selection (Darwin proposed that evolution is ...
user501885's user avatar
0 votes
1 answer
165 views

About another potential characterization of normal numbers

Normal numbers, in a nutshell, are real numbers that have a "uniform" distribution of digits in standard numeration systems (binary, decimal, and so on.) You can find a formal definition and ...
Vincent Granville's user avatar
0 votes
1 answer
101 views

multimodal circular model

Hi, can someone provide me with a list of probability models that is akin to Von Mises but consists multiple (potentially infinite) modes that takes into account attractors in the entire 2-D spatial ...
user22624's user avatar
0 votes
1 answer
177 views

Under which conditions Mean Square Continuity implies Sample Continuity for Gaussian Processes?

First, let us give the setting. Let $(\Omega, \Sigma, \mathbf{P})$ be a probability space, let $T$ be some interval of time, and let $X: T \times \Omega \rightarrow S$ be a stochastic process. By Mean ...
Grandes Jorasses's user avatar