MCMC | Monte Carlo and Markov Chain Monte Carlo
kandi X-RAY | MCMC Summary
kandi X-RAY | MCMC Summary
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
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Top functions reviewed by kandi - BETA
- Use rejection sampling
- Compute the probability of x
- Generate samples using gibbs sampling
- Create a random vector from the given parameters
- Predict a random vector at x
- Function for j_j function
- Function to log - likelihood function
- Return the value of the function N
- Generate random sample sampling
- Inverse of p_inv
- Takes a proposal value
- Generate the metropolis hastings
- Quaternion PDF
MCMC Key Features
MCMC Examples and Code Snippets
Lines of Code : 51
License : No License
Copydef play_one_mc(env, pmodel, vmodel, gamma):
observation = env.reset()
done = False
totalreward = 0
iters = 0
states = []
actions = []
rewards = []
reward = 0
while not done and iters < 2000:
# if we reach 2000, just quit, Community Discussions
Trending Discussions on MCMC
QUESTION
I want to estimate parameters of negative binomial distribution using MCMC Metropolis-Hastings algorithm. In other words, I have sample:
...ANSWER
Answered 2022-Jan-19 at 21:25Change dnorm in loglikelihood to dnbinom and fix the proposal for prob so it doesn't go outside (0,1):
QUESTION
I have a M1 Macbook Pro running OS Big Sur and just tried to install rjags and JAGS. I downloaded JAGS from https://sourceforge.net/projects/mcmc-jags/ without a problem, and ran install_packages("rjags"), but when I run library(rjags) I get this error:
ANSWER
Answered 2021-Dec-30 at 23:13See here for a similar post.
So have you installed the correct version of JAGS on your Apple computer?
Essentially, rjags is an R interface and it needs an installed JAGS (a back-end you mentioned) to function.
Follow the instructions here to install JAGS on Apple computers with M1 chips.
Also from the link above (from JAGS development team):
A readme file is provided in the disk image - please do read it.
If you find that rjags fails to load after installation of JAGS, make sure you have installed the Mavericks or El Capitan binary of R from CRAN. If you really need the Snow Leopard build of R (or if you compiled R yourself) you will have to compile JAGS from source.
If you have followed the instructions above (and in the relevant README file) and are still having problems with installation of these binaries, please let us know via the JAGS discussion forum.
PS: I would comment if I could, but I am new here and don't have enough points to do so.
QUESTION
Suppose the random variable X ∼ N(μ,σ2) distribution
and I am given the observations x =(0.7669, 1.6709, 0.8944, 1.0321, 0.0793, 0.1033, 1.2709, 0.7798, 0.6483, 0.3256)
Given prior distribution μ ∼ N(0, (100)^2) and σ2 ∼ Inverse − Gamma(5/2, 10/2).
I am trying to give a MCMC estimation of the parameter.
Here is my sample code, I am trying to use Random Walk MCMC to do the estimation, but it seems that it does not converge:
...ANSWER
Answered 2021-Nov-11 at 10:55There are a few problems with the code.
You calculate the log-likelihood, but ignore the log-prior. So effectively you're using a uniform prior, not the one you specified.
The initial calculation of the log likelihood has parentheses in the wrong place:
QUESTION
I'm trying to include a black box likelihood function in a pymc3 model. This likelihood function just takes a vector of parameter values and returns the likelihood (all data is already included in the function).
So far I've been following this guide and have modified the code as follows to accommodate the fact my model only has one parameter k.
...ANSWER
Answered 2021-Nov-05 at 09:42As per the comments I checked out this thread and discovered that pm.potential really was the cleanest way to achieve black-box likelihood. Modifying the code above as follows did the trick:
QUESTION
I am trying to use pyro for specifying a Bayesian network. I have a child node D which is continuous and it has three discrete nodes are parents, each with 10 possible states:
So, I first define my discrete nodes as:
...ANSWER
Answered 2021-Oct-06 at 21:17Good thing about pyro is that the model definition is very pythonic. The underlying PyTorch machinery can track dependencies for you.
All you need is use the samples A, B and C and compute the parameters of the conditional p(D|A,B,C)
QUESTION
I wanted to use priors on hyper-parameters as in (https://gpflow.readthedocs.io/en/develop/notebooks/advanced/mcmc.html) but with an SVGP model.
Following the steps of example 1, I got an error when I run de run_chain_fn :
TypeError: maximum_log_likelihood_objective() missing 1 required positional argument: 'data'
Contrary to GPR or SGPMC, the data are not an attribut of the model, they are included as external parameter.
To avoid that problem I modified slightly SVGP class to include data as parameter (I don't care with mini-batching for now)
...ANSWER
Answered 2021-Aug-30 at 18:18SVGP is a GPflow model for a variational approximation. Using MCMC on the q(u) distribution parameterised by q_mu and q_sqrt doesn't make sense (if you want to do MCMC on q(u) in a sparse approximation, use SGPMC).
You can still put (hyper)priors on the hyperparameters in the SVGP model; gradient-based optimisation will then lead to the maximum a-posteriori (MAP) point estimate (as opposed to pure maximum likelihood).
QUESTION
I had a model and some observational data. I used the MCMC method to obtain the best free parameters and used some coding to plot contours of 1 to 3 sigma confidence levels (as you see in the plot). I want the +/- value of sigma for each best value in any confidence level but I know that it is not symmetric. So, I didn't find the common equation of square variance useful. Is there any other way to calculate +/- errors? this is my contours
...ANSWER
Answered 2021-Aug-08 at 20:54I used np. percentile(w, [15, 85]), np. percentile(w, [5, 95]) and np. percentile(w, [0.5, 99.5]) in the final Gaussian probability and found errors for each confidence level correctly
QUESTION
I am trying to do MCMC methods on the inverse problem of A(u)x=b, where A is a symmetric positive definite square matrix. I was given that A can be expressed as A = A0 + Σi ui Ai. I want to check whether the ergodic average converges to the initial u. But I need to create some random matrix A satisfying the conditions to test out my MCMC function.
Is it possible to create such a random matrix A that is of the form of A = A0 + Σi ui Ai and how can I go about it in python?
Any help is greatly appreciated, thank you!
...ANSWER
Answered 2021-Jul-13 at 08:05One way would be to generate SPD (symmetric positive definite) matrices A[0],A1,.. and positive numbers u1,.. and then sum them up
QUESTION
I'm trying to simulate the evolution of stock prices, so I decided to use a MCMC simulation (I don't know if there's a library that could help me with it though). What I intend to do is:
- Create a function for simulating the evolution of a stock price, given a number of parameters:
ANSWER
Answered 2021-Jun-25 at 16:51This would solve the map problem:
QUESTION
ANSWER
Answered 2021-Jun-24 at 22:10Did you try to save the images with another resolution, using for example dpi=600 as parameter in fig.savefig?
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install MCMC
You can use MCMC like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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