mcmc's questions - Chinese 1answer

922 mcmc questions.

I'm analyzing codon usage using the model described in the following paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494061/ In brief, this model uses a MCMC to estimate the pausing time for the ...

I was browsing the github repo for Pymc and found this notebook: Variational Inference: Bayesian Neural Networks The author extols the virtues of bayesian/probabilistic programming but then goes on ...

I have been applying [https://docs.pymc.io/notebooks/rugby_analytics.html][1] to my data. The problem is that I also have win/ lose probabilities from another model along with the home and away scores....

I learned about MCMC and variational inference for Bayesian inference, and I would like to try it out in some regression problem. However, all existing related models I know falls within either of the ...

I watched a video on coursera, everything went well until the following slide around 12'50''. I read it in other papers that to estimate latent variables say $\Phi$ we can draw a sample $Z'$ of the ...

In In the gamm4/lme packages in R, I am a bit confused about hwo to set the number of knots for splines. Suppose that $Y$ is my ...

Marginal likelihood evaluation for a Poisson data model. Simulate 10 observations from a known Poisson distribution with expected value 2. Use a Gamma(1,1) prior distribution and compute the ...

I have the polynomial regression model $Y_i | \mu, \sigma^2 \sim \mathcal{N}(\mu_i, \sigma^2), i = 1, \dots, n \ \text{independent}$ $\mu_i = \alpha + \beta_1 x_{i1} + \beta_2 x_{i2} + \beta_3 x^2_{...

I'm wondering what's wrong with this MCMC algorithm. I use independent normal proposal to sample from bi-normal mixture, but the result I've got is unimodal. ...

Can the acceptance rate in MH algo be greater than 1? When that case occurs the proposal will off coruse be accepted with probability 1. But is it "ok" to allow a acceptance rate greater than 1?

Basic question about MCMC Metropolis–Hastings algorithm. I am trying to understand the Metropolis–Hastings algorithm and it's connection to Bayesian Analysis. Suppose I want to construct an MCMC MH ...

I am currently in the process of implementing a model for soccer result prediction in JAGS. Actually, I have implemented several, but I have reached my most difficult challenge yet: A model described ...

I have a proposal distribution for one parameter theta_guesstheta_guess = guessleft(theta_accept(1,r-1), 0.01,0)which is a ...

This is a recurring question (see this post, this post and this post), but I have a different spin. Suppose I have a bunch of samples from a generic MCMC sampler. For each sample $\theta$, I know the ...

Suppose we want to estimate posterior variance of $\alpha$ given x, i.e. var($\alpha|x$). We have MCMC posterior samples $a_1,\dots, a_B$, which are not independent. Does $\hat{\text{var}}(\alpha|x) =...

Is MCMC-based mixed model with flat prior basically just a robust variant of a classical mixed model? I mean – frequentist analyses work with a flat prior anyway so the only difference should be in ...

I have read something like 6 articles on Markov Chain Monte carlo methods, there are a couple of basic points I can't seem to wrap my head around. How can you "draw samples from the posterior ...

my question is probably amateurish but I can't seem to find the answer anywhere. In what metric are the MCMCglmm package's posterior means for family = "categorical"? I suppose that they can't be ...

my question is probably amateurish but I can't seem to find the answer anywhere. In what metric are the MCMCglmm package's posterior means for ...

Is there a library, or package---preferably in (but not restricted to) python or R---that let you easily sample from the posterior of "exotic" distributions, i.e. distributions that are not commonly ...

Background I had an example that sought to demonstrate the posterior predictive distribution in the context of a normal measurement model. The data that was used is as follows: ...

I've a question regarding the fitted values of a binomial model using the brms package. I have this code: ...

I'm looking to compute $n\text{Var}\left(\frac{1}{n}\sum_{i=1}^nX^{(i)}\right) = \frac{1}{n}\sum_{i=1}^n\sum_{j=1}^n\text{Cov}\left(X^{(i)},X^{(j)}\right)$ in R. Assuming the X's not to be iid, we get ...

I have the following two Markov chains: 1. 2. I'm trying to characterise them. Unfortunately, I have no idea how to "characterise" them. At best, I can tell that chain 2 looks a lot "healthier" ...

I'd like to calculate the posterior mode (maximum a posteriori estimate) for $\Pr[\,X \,|\, Y\,]$ for a model where $X$ is generated by an Ising model on a two-dimensional lattice and $Y$ are noisy ...

I'm trying to use Stan and R to fit a model that, uhh, models the observed realisations $y_i = 16, 9, 10, 13, 19, 20, 18, 17, 35, 55$, which are from a binomial distributed random variable, say, $Y_i$,...

I have a question on how to fit a censoring problem in JAGS. I observe a bivariate mixture normal where the X values have measurement error. I would like to model the true underlying 'means' of the ...

I am dealing with a fitting problem. Specifically, I am fitting a Lorentzian profile to the power spectrum of an solar-like oscillating star. Three parameters in the Lorentzian profile characterize ...

I am using the Poisson form of the software provided by Wallstrom et al to model some time-series data which should be well modelled by a non-stationary Poisson process. One of the main parameters ...

I'm trying to reproduce Figure 2 from this paper. In summary, I have the regression model $$Y \sim N(\mu, \sigma^2) \\ \mu = \alpha + \beta_1X_1 + \beta_2X_2 $$ The prior distributions for the ...

Why does the indicator function is equivalent to the integral over the Dirac mass? In my lecture notes the proof for the Kernel of the Metropolis Hastings is given as follows: $$P(X^t \in \mathcal{X}...

Is it feasible (in terms of computational time) to compute a posterior distribution involving 10 parameters? The time to calculate the likelihood function is about 1 minute, as likelihood is computed ...

I'm going to try to provide a better explanation of the model, so the problem gets clearer. I'm trying to forecast the lower part of a runoff triangle, which is simply a form used to display the data ...

I'm working on wireless sensor networks and I wish to be capable of detecting if there are any outliers in the sensed data as well as imputing missing ones. I read a lot of articles which made me more ...

Consider $N$ observed data points $x_i$ ($i=1,..,N$), and a likelihood that depends on $p$ parameters: $f(x_i|\theta_n)$ ($n=1,..p$). From Bayes' theorem $$p(\theta_n|x_i) = \frac{f(x_i|\theta_n)g(\...

Homework question: Consider the 1-d Ising model. Let $x = (x_1,...x_d)$. $x_i$ is either -1 or +1 $\pi(x) \propto e^{\sum_{i=1}^{39}x_ix_{i+1}}$ Design a gibbs sampling algorithm to generate ...

I am trying to detect convergence of a random walk on a graph. After doing some preliminary research, the Geweke convergence diagnostic seems to be most commonly used for this. This diagnostic calls ...

I'm using Hamiltonian Monte Carlo (HMC) implementation in Edward, a probabilistic programming library built on top of TensorFlow. One of the hyper-parameters of HMC is the step size: ...

I have changed my MCMC sampler from Ensemble to Parallel-Tempered (in emcee) in order to get an estimate of the evidence integral. In practice this requires setting ...

Say I have some samples from a distribution $p$, and I want to get more samples using MCMC/Gibbs sampling. Since the existing samples are known from the equilibrium distribution $p$, if I use them as ...

I have 4000 iterations from an MCMC summarizing the posterior distribution. In the model used to estimate the posterior (a GLMM), my response variable was in g/m2, but I need to report the result in ...

Intuitively, if I want to update two parameters in one step, I have to come up with a proposal that are good for both parameters. Assuming that the parameters are independent, is it correct to ...

Suppose that in a Bayesian framework we have observed data $D$, using independent prior distributions on the parameters of the model, denoted by $\theta_1, \theta_2$. Then, the joint posterior ...

I have two 2D histograms - one has observed counts and the other has predicted counts from a model. I am comparing both of them using a Poisson likelihood while varying the parameters of the model. ...

I run a MCMC in r and along with the parameter mean values from the posterior I also got the standard deviation, the naive standard error of the mean and the relative numerical efficiency. How is ...

Is it possible to plot log likelihood function evolution in mcmc simulations? I have a mixture model and its parameters are estimated using the gibbs sampling method in r environment and using the ...

I have a data set which contains closing prices of a stock every day (total 1 year). Can i forecast that set like 1 or 2 year with using Markov methods? If yes, then how?

I am interested in using a Linear Mixed Effects model to analyse some data with 3 nested random factors to evaluate which factor(s) contribute most to the variability observed. The data has a log-...

I'm studying Carter and Kohn's (1994) implementation of the Gibbs sampler for Bayesian analysis of state space models. In their paper, they assume the starting value, call it $\beta_0$, of the state ...

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