mcmc

问题 I need to extract the posterior estimates and intervals for a random effect from my model. For illustrative purposes, a similar dataset to the one I am using would be the ChickWeight dataset in base R. The way I extract the posterior estimates and intervals for my fixed effects is like so: #load package library(lme4) #model m.surv<-lmer(weight ~ Time + Diet + (1|Chick), data=ChickWeight) #load packages library(MCMCglmm) library(arm) #set up for fixed effects sm.surv<-sim(m.surv) smfixef.surv

问题 I need to extract the posterior estimates and intervals for a random effect from my model. For illustrative purposes, a similar dataset to the one I am using would be the ChickWeight dataset in base R. The way I extract the posterior estimates and intervals for my fixed effects is like so: #load package library(lme4) #model m.surv<-lmer(weight ~ Time + Diet + (1|Chick), data=ChickWeight) #load packages library(MCMCglmm) library(arm) #set up for fixed effects sm.surv<-sim(m.surv) smfixef.surv

问题 I'm using an example of linear regression from bayesian methods for hackers but having trouble expanding it to my usage. I have observations on a random variable, an assumed distribution on that random variable, and finally another assumed distribution on that random variable for which I have observations. How I have tried to model it is with intermediate distributions on a and b , but it complains Wrong number of dimensions: expected 0, got 1 with shape (788,). To describe the actual model,

来源： https://stackoverflow.com/questions/64287562/lapply-instead-of-for-loop-for-randomised-hypothesis-testing-r

问题 I'm trying to explain changes in tree vitality from 1 to 3 (1=green, 2=damage, 3=dry) using climatic variables in an MCMCglmm model. Unfortunately, I am struggling with two questions: 1. How do I interpret the summary of the MCMCglmm model? I see which variables are significant, but what does it mean? Does this mean that with increasing precipitation the vitality gets to 1 (green)? I plotted the posterior means of the model and looks like precipitation and diameter have no influence on the

问题 I'm trying to explain changes in tree vitality from 1 to 3 (1=green, 2=damage, 3=dry) using climatic variables in an MCMCglmm model. Unfortunately, I am struggling with two questions: 1. How do I interpret the summary of the MCMCglmm model? I see which variables are significant, but what does it mean? Does this mean that with increasing precipitation the vitality gets to 1 (green)? I plotted the posterior means of the model and looks like precipitation and diameter have no influence on the

问题 I've recently started learning pymc3 after exclusively using emcee for ages and I'm running into some conceptual problems. I'm practising with Chapter 7 of Hogg's Fitting a model to data. This involves a mcmc fit to a straight line with arbitrary 2d uncertainties. I've accomplished this quite easily in emcee , but pymc is giving me some problems. It essentially boils down to using a multivariate gaussian likelihood. Here is what I have so far. from pymc3 import * import numpy as np import

问题 I'm trying to put together a model of a dynamical system in PyMC3, to infer two parameters. The model is the basic SIR, commonly used in epidemiology : dS/dt = - r0 * g * S * I dI/dt = g * I ( r * S - 1 ) where r0 and g are parameters to be inferred. So far, I'm unable to get very far at all. The only examples I've seen of putting together a Markov chain like this yields errors about recursion being too deep. Here's my example code. # Time t = np.linspace(0, 8, 200) # Simulated observation

问题 I used to code my MCMC using C. But I'd like to give PyMC a try. Suppose X_n is the underlying state whose dynamics following a Markov chain and Y_n is the observed data. In particular, Y_n has Poisson distribution with mean depending on X_n and a multidimensional unknown parameter theta X_n | X_{n-1} has distribution depending on theta How should I describe this model using PyMC? Another question: I can find conjugate priors for theta but not for X_n. Is it possible to specify which

问题 I used to code my MCMC using C. But I'd like to give PyMC a try. Suppose X_n is the underlying state whose dynamics following a Markov chain and Y_n is the observed data. In particular, Y_n has Poisson distribution with mean depending on X_n and a multidimensional unknown parameter theta X_n | X_{n-1} has distribution depending on theta How should I describe this model using PyMC? Another question: I can find conjugate priors for theta but not for X_n. Is it possible to specify which