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ReturnDynamicsMCMC

: Implementing MCMC algorithms to find parameters in Return Dynamics

[ How to run ]

  • "setup.txt" in the main directory is the setting file for how to implement MCMC
  • It contains the options that choose either performing simulation or using observed data, MCMC initial guess for paramters and hyperparameters
  • Using the shell script below, you may implement MCMC and have figures that summarize final results
:~$ ./run.sh

[ Summary for the parameters from MCMC implementations ]

table

[ Comparison between the true and estimated mu ]

The last mu

mu_last

The last 100 iteration-averaged mu

mu_mean

[ Chainplot for each parameter in the total iterations ]

E_mu

beta

sigma_y

sigma_mu

rho

[ Chainplot for each parameter in early stage of iterations ]

E_mu_f

beta_f

sigma_y_f

sigma_mu_f

rho_f

[ Prerequisites for this code in Ubuntu ]

Core library needed for C++

  • Eigen3
:~$ sudo apt-get install -y apt-get install libeigen3-dev

Graphic tool in Python script

  • Python3
sudo apt-get install -y python3 python3-pip python3-dev python3-env
  • Numpy & Scipy
sudo pip install numpy scipy
  • Matplotlib
sudo pip install matplotlib

returndynamicsmcmc's People

Contributors

minsung-jang avatar

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