# Simulation and Inference for Stochastic Processes
**URL:** https://discourse.julialang.org/t/simulation-and-inference-for-stochastic-processes/22020
**Category:** General Usage
**Tags:** question
**Created:** [March 19, 2019, 6:59am UTC](https://discourse.julialang.org/t/simulation-and-inference-for-stochastic-processes/22020 "2019-03-19T06:59:36Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@Yifan\_Liu](https://discourse.julialang.org/u/Yifan_Liu)
#### Post date: [March 19, 2019, 6:59am UTC](https://discourse.julialang.org/t/simulation-and-inference-for-stochastic-processes/22020/1 "2019-03-19T06:59:37Z")
Is there any package that allows me to do simulation and inference for stochastic processes?
For example, if I need to estimate parameters for Merton (1976) jump diffusion model using stock return data, which package should I use? I am looking for something similar to yuima:
[https://yuimaproject.com/](https://yuimaproject.com/)
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### Author:  [@Tamas\_Papp](https://discourse.julialang.org/u/Tamas_Papp)
#### Post date: [March 19, 2019, 7:34am UTC](https://discourse.julialang.org/t/simulation-and-inference-for-stochastic-processes/22020/2 "2019-03-19T07:34:22Z")
> [@Yifan\_Liu](#):
>
> estimate parameters for Merton (1976) jump diffusion model using stock return data, which package should I use?
It depends on the kind of inference you want to perform, you have to be more specific (eg ML, MAP, Bayesian, indirect inference, …). There are packages to help doing these, but you have to understand the relevant methodology (eg for MAP, you have to be able to code a likelihood, then use on of the optimization packages). I am not aware of a pre-packaged black box approach.
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### Author:  [@Yifan\_Liu](https://discourse.julialang.org/u/Yifan_Liu)
#### Post date: [March 19, 2019, 1:04pm UTC](https://discourse.julialang.org/t/simulation-and-inference-for-stochastic-processes/22020/3 "2019-03-19T13:04:58Z")
That is what I am doing now, when I do it in optimization ways, I am not sure how to find the p value and the choice of initial guess affect results a lot. That’s why I want a black box that could handle this.
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### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [March 19, 2019, 4:03pm UTC](https://discourse.julialang.org/t/simulation-and-inference-for-stochastic-processes/22020/4 "2019-03-19T16:03:34Z")
> [@Yifan\_Liu](#):
>
> Is there any package that allows me to do simulation and inference for stochastic processes?
DifferentialEquations.jl. Simulating jump diffusions:
[http://docs.juliadiffeq.org/latest/tutorials/jump\_diffusion.html](http://docs.juliadiffeq.org/latest/tutorials/jump_diffusion.html)
Estimation:
[http://docs.juliadiffeq.org/latest/analysis/parameter\_estimation.html#Parameter-Estimation-for-Stochastic-Differential-Equations-and-Monte-Carlo-1](http://docs.juliadiffeq.org/latest/analysis/parameter_estimation.html#Parameter-Estimation-for-Stochastic-Differential-Equations-and-Monte-Carlo-1)