Questions tagged [stochastic]

For questions regarding the numerical treatment of processes whose behaviors are determined by both deterministic (predictable) and non-deterministic (random) actions.

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How can I generate time series band-limited white noise for a given give voltage-amplitude distribution over desired frequency band in Python?

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1 vote
2 answers
83 views

Simulate Jump-Diffusion $dX_t = \mu(X_t)dt + \sigma(X_t)dW(t) + \int_{\{|c| <1 \}}g(X_t,c)\tilde{N}(dt,dc) + \int_{\{|c| \ge 1 \}}h(X_t,c)N(dt,dc)$

I would like to be able to model an SDE having the form $$dX_t = \mu(X_t)dt + \sigma(X_t)dW(t) + \int_{\{|c| <1 \}}g(X_t,c)\tilde{N}(dt,dc) + \int_{\{|c| \ge 1 \}}h(X_t,c)N(dt,dc).$$ where $W$ is a ...
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4 votes
1 answer
76 views

Stochastic cellular automata - algorithm limited by 1 cell per timestep

Context Let's say I am trying to model the spread of mold in a petri dish, using a stochastic cellular automata approach. The petri dish can be thought of as a grid of 1mm x 1mm squares, each called ...
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1 vote
0 answers
102 views

Stochastic differential equation system (SDE) : overflow encountered in double-scalars

I'm trying to integrate the following SDE system from Dekker et al. [1] $$\begin{cases} \frac{dx}{dt}=a_1x^3+a_2x+\phi+\zeta_x\\ \frac{dy}{dt}=b_1z+b_2(\kappa(x)-(y^2+z^2))y+\zeta_y\\ \frac{dz}{dt}=...
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1 vote
1 answer
73 views

Rate of convergence - Stochastic Euler Method

The absolute error criterion of the pathwise approximation of an Ito process $X$ by an Euler approximation $Y$ is: $$ \epsilon=E\left(\left|X_{T}-Y(T)\right|\right) $$ We shall say that a time-...
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1 vote
1 answer
53 views

Numerical stability of taking the `mean` of outputs from the simulation of a discrete stochastic dynamical system

I am writing a simulation for a discrete stochastic dynamical system. Since the simulation is stochastic, I need to run the simulation multiple times and then average the values of each timestep. I ...
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4 votes
1 answer
48 views

Guidelines for publishing data from a stochastic simulation

So, my question is if one should ideally keep a record of all seeds that are used when publishing numerical work that involves one or more random number generators (e.g. a stochastic simulation), and ...
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  • 161
2 votes
1 answer
78 views

Adaptive Runge-Kutta for Stochastic (Projected) Gross-Pitaevskii Equation

I am using the XMDS library for solving the stochastic (projected) Gross-Pitaevskii equation $$i \hbar \partial \Phi\left(\mathbf{r},t\right)_t=\hat{\mathcal{P}}\left\{(1-i \gamma)\left(\hat{H}_{\...
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1 answer
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Numerically solving nonlinear parabolic stochastic PDEs

For a project I'm doing, I have to numerically solve a nonlinear parabolic stochastic partial differential equation, of the form $$ u_t = u_{xx} + f(u)(u_x)^2 + a(u) + b(u)W(t, x), $$ where primes ...
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1 vote
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Effective fitness formula in the Moran process on a game

I was recently reading some literature about evolutionary game theory and I got particularly interested in Moran process linked to prisoner's dilemma as a model of evolution of two sub-populations. ...
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5 votes
0 answers
88 views

Probability approximation: monte carlo VS sde

I have a probability measure $\mu$ (say, in $\mathbb{R}^{d}$, with density) and I want to approximate it numerically. Today I noticed that my measure is ergotic for a certain Stochastic Differential ...
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2 votes
0 answers
104 views

Numerical integration of SDE: choice of $dt$ and algorithm

I am working on the following Stochastic Differential Equation (SDE) in the Quantum Mechanics context: $$dX_{t} = a X_{t} dt + b X_{t} dW$$ where $X_{t}$ is my stochastic varible, $dt$ is my ...
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1 vote
1 answer
60 views

What is the right way to set up two random tensor fields which have an identical average diffusivity

I want to compare some properties of traveling waves through two randomly diffusive media. The traveling waves follow the fisher equation: $$\frac{du}{dt} = \nabla(\mathbf{D}_{\gamma} \nabla u) + u(1-...
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3 votes
2 answers
207 views

How to optimize sampling for parameter estimation

I have a computer model with a number of parameters that need to be calibrated based on experimental results. It's also important to understand the sensitivity of the results to each parameter ...
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2 votes
1 answer
97 views

How to get started with numerically solving a Stochastic Navier Stokes equation

I originally posted the question on the math stackexchange, and was told I should try here. I’m researching Stochastic PDE, in particular the Navier Stokes Equation, and would like to estimate the ...
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2 votes
1 answer
1k views

Simultaneously maximize and minimize

I am virtually new to optimization (saw it years ago in a very shallow course) and now I came across a problem that I believe would require from it. The problem is I don't know exactly how to proceed. ...
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2 votes
0 answers
74 views

How to determine the order of convergence of the Euler-Maruyama method?

This question is originally posted in Quant.StackExchange but has been unanswered for some time so I ask in here. To make this simple let us consider the Geometric Brownian Motions (GBM). My ...
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2 votes
0 answers
46 views

Is this a form of stochastic gradient descent?

I want to minimize the following with respect to parameters $B$. $$\sum_{k = 1}^{K} f(A_{k}, B)$$ where $A_k$ are $K$ different data-sets and $B$ is a matrix of parameters. Can I do this by a ...
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  • 163
5 votes
0 answers
42 views

Stochastic conjugate directions to improve convergence in narrow valleys

My question concerns a specific statement in this paper: N. N. Schraudolph and T. Graepel, "Conjugate Directions for Stochastic Gradient Descent," in Int. Conf. Artificial Neural Networks, Berlin, ...
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  • 51
2 votes
1 answer
171 views

Jump-Diffusion process: practical solver beyond Euler method?

A jump-diffusion process is a stochastic process where both continuous noise (in my case complex Wiener noise $dZ,dZ^*$ such that $dZ^2=dZ^{*2}=0,|dZ|^2=dt$) and discrete Jumps (in my case Poissonian $...
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  • 123
3 votes
1 answer
111 views

Computation of the heat kernel from Brownian motion

This question is rather simple but I have some difficulties to find code. Let us suppose that I wrote a routine, in a given language, that computes the evolution of a particle doing Brownian motion in ...
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  • 203
0 votes
1 answer
51 views

Implementation of stochastic cellular automata

In my problem, I have a lattice with a stochastic cellular automaton. In order to simplify a bit, let's say it is 1D. In my system, each node can be type A, B or C. A way to represent the system and ...
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1 vote
1 answer
54 views

Open source solver for continuous-time stochastic non-linear DAEs (SDAEs)

I am trying to solve a system of non-linear index-1 DAEs in which the derivatives of the state variables, $x(t)$ are corrupted by additive noise, $w(t)$ (whose covariance matrix is known). $\dot x(t) =...
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3 votes
2 answers
410 views

Runge Kutta and Milstein – system of second-order coupled differential equations with noise

I would like to solve a system of second-order differential equations to describe the dynamics of a system of particles. Two Newton-like forces are responsible for the motion of each particle $i$: A ...
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3 votes
1 answer
62 views

Linear programming with stochasticity?

Suppose I have implemented an LP, where some constraint coefficients are implemented as the mean of some probability distribution. Now, I would like to solve the same problem but with stochasticity ...
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1 vote
1 answer
139 views

How is KDE used in stochastic tomography

I am currently writing my masters thesis and my topic also touches on Stochastic Tomography for volume reconstruction presented in this paper. Now i understand most of the process described, but i ...
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8 votes
2 answers
208 views

What’s so great about derivative-free solvers for SDEs?

I am trying to familiarise myself with SDEs and have been reading a few review papers on the topic. They leave the impression that a great deal of work has been put into solvers that are derivative-...
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3 votes
0 answers
131 views

Stochastic gradient descent for large deterministic optimization problems

The Wikipedia page for SGD describes optimizing a function $f = \sum f_i(\theta;x_i)$ by successively approximating gradients from random subsets of the data, while most literature poses the problem ...
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2 votes
0 answers
79 views

Global optimization with known distributions of some variables

I'm solving simple single-objective multidimensional global optimization problem using various stochastic algorithms like Monte-Carlo, GA and other evolutionary approaches. The task is formulated as ...
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2 votes
1 answer
83 views

From deterministic to stochastic LP formulations

I am having a hard time understanding the very first example in "A Tutorial on Stochastic Programming". More specifically the authors show that one can formulate the stochastic variant of (1.2) ...
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2 votes
2 answers
170 views

What kind of optimisation algorithm is suitable for a computationally expensive function?

I have a reference value $R$ and a modelled value $M$. $M$ is generated using a stochastic algorithm with parameters $a$ and $b$. The objective is to tune $a$ and $b$ so that $M$ is as close as $R$ ...
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2 votes
1 answer
220 views

Problem with Richardson extrapolation method for weak convergence in SDE

I have implemented the Richardson extrapolation of the Euler-Maruyama method to 4th order, to estimate the moments of SDE. The Euler-Maruyama works, and I would expect the Richardson extrapolation to ...
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  • 123
1 vote
1 answer
2k views

Solving second order SDE with Gaussian white noise for first time derivative in Matlab

I'm having trouble solving a second order differential equation with Gaussian white noise. The equation I'm solving follows the form: $$Ax'' + Bx' + \sin(x) = i + i_{n}$$ where $i_{n}$ is the ...
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2 votes
0 answers
99 views

non convex, non linear optimization involving matrix differential equation solution

I'm trying to develop an inferential procedure for a multivariate dependent Markov process. Basically, the procedure could be considered as a non linear regression, with a known dependence structure ...
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7 votes
4 answers
7k views

Algorithm for high quality 1/f noise?

How can I generate arbitrarily high quality $1/f$ noise, for use in a model? My model involves a lot of feedback, over a large number of iterations, with a very high bandwidth, so I'd like the $1/f$ ...
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  • 173
2 votes
0 answers
86 views

How to fix time intervals to store data in a stochastic simulation (continous time markov chain)

I am using FORTRAN to implement Gillespie's stochastic simulation algorithm. I would be running many simulations in parallel (both parallel instances with different seed and parallel functions); if I ...
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  • 143
3 votes
1 answer
1k views

How to integrate numerically a function with error bars?

Typically, the function that one wants to integrate numerically, $f$, is given, i.e. its values for various points $\{x_i\}$ are known precisely. The resulting error is due to the fact that we chose a ...
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  • 151
1 vote
0 answers
2k views

How to solve an ode with stochastic time-dependent input

I am trying to repeat an example I found in a paper. I have to solve this ODE: $25 \ddot{x} + 15 \dot{x} + 330000 x = p(t)$ where $p(t)$ is a white noise sequence band-limited into the 10-25 Hz ...
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  • 382
2 votes
1 answer
369 views

Is Langevin thermostat/equation correct when trying to model time-dependent behaviour of a molecule?

I've been taught that when simulating a biomolecule in thermal equilibrium, it's best to use the Langevin thermostat - an algorithm which produces a trajectory, which is a realization of a stochastic ...
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  • 151
2 votes
0 answers
67 views

Solving a nonlinear equation with a Markov process and RVs

Assume that we have the following equation and the following assumption. The scope is to solve for some particular variables expressed later. Update $$E_{t}\left[ b(A_{t+1})^{1-\gamma} *R_{t+1}^{-\...
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  • 235
4 votes
1 answer
240 views

Solving a nonlinear equation with random variable

I would like to solve an equation that looks like this UPDATE $E[(R^{1-\gamma})(r_k+\theta-r_z)]=0$ , where $R=\phi r_z+(1-\phi)(r_k+\theta)$ and $\phi\in[0,1]$, $\theta$, is a random variable ...
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  • 235
2 votes
2 answers
359 views

Extracting time scales information from empirical cumulative distribution function

I have a stochastic process (a Markov chain actually) that has two absorbing states. I am using a difference equation to calculate the first passage time to either of the absorbing states. There are ...
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  • 269
2 votes
2 answers
2k views

visualization of 3D probability flow

I have a master equation for $P(N_A^+,N_B^+,N_C^+,t)$, with $N_A^+,N_B^+,N_C^+$ all discrete. The numerical integration is done by this Matlab program using Euler's method. Despite the crude Euler's ...
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  • 269
4 votes
1 answer
438 views

Slight mistake in Stochastic Galerkin code

I'm following Paul Constantine's Primer on Stochastic Galerkin Method, Section 3.1 (2D Poisson Example). In this matlab code, the example attempts to solve the PDE $$\alpha(w)(u_{xx}+u_{yy})=1 \text{ ...
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3 votes
0 answers
87 views

Stochastic Collocation for time evolving ODE

For an Stochastic Differential Equation, e.g., $$ \frac{du}{dt} = \alpha*\sin(u*t) $$ where $\alpha$ is normally distributed with nonzero mean, I am trying to use a stochastic collocation approach ...
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  • 31
7 votes
2 answers
1k views

Examples of numerical solution of stochastic differential equation(SDE)?

I want to simulate a nonlinear stochastic differential equation $$ {\rm d}X_t = f(X_t) {\rm d}t + g(X_t){\rm d}B_t $$ where $f,g \in C^{\infty}({\mathbb R}^n ,{\mathbb R})$ and $B_t$ is one-...
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11 votes
2 answers
528 views

Numerical method for equation solving that works on stochastically computed functions

There are many well known numerical methods for solving equations of the type $$ f(x) = 0, \quad x \in \mathbb{R}^n,$$ e.g. bisection method, Newton's method, etc. In my application $f(x)$ is ...
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  • 2,550
5 votes
2 answers
294 views

Convergence tests in Markov Chain Monte Carlo

For a relatively simple Markov chain Monte Carlo process, such as using Metropolis to find calculate thermal averages for an Ising model, how is it possible to determine whether quantities have ...
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2 votes
1 answer
172 views

How to solve this numerical technique problem?

Well, in a numerical technique test we were given the following problem: A physical phenomenon is modeled such that, $F(f,d) = A(f)/d^2 + L$; Where, $F$ is a function of frequency $f$ and distance $...
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  • 121
6 votes
0 answers
678 views

Stochastic Galerkin projection approach for using generalized polynomial chaos expansion (GPCE) in solving PDE

I want to know if there is any way to define the test and trial function in the way that I want instead of using the default functions. So if I want define the polynomial and basis and coefficient, ...
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