pymc3

问题 I've tried to fit a simple 2D gaussian model to observed data by using PyMC3. import numpy as np import pymc3 as pm n = 10000; np.random.seed(0) X = np.random.multivariate_normal([0,0], [[1,0],[0,1]], n); with pm.Model() as model: # PRIORS mu = [pm.Uniform('mux', lower=-1, upper=1), pm.Uniform('muy', lower=-1, upper=1)] cov = np.array([[pm.Uniform('a11', lower=0.1, upper=2), 0], [0, pm.Uniform('a22', lower=0.1, upper=2)]]) # LIKELIHOOD likelihood = pm.MvNormal('likelihood', mu=mu, cov=cov,

问题 I try to predict the outcome of soccer games based on the number of goals scored and I use the following model: with pm.Model() as model: # global model parameters h = pm.Normal('h', mu = mu, tau = tau) sd_a = pm.Gamma('sd_a', .1, .1) sd_d = pm.Gamma('sd_d', .1, .1) alpha = pm.Normal('alpha', mu=mu, tau = tau) # team-specific model parameters a_s = pm.Normal("a_s", mu=0, sd=sd_a, shape=n) d_s = pm.Normal("d_s", mu=0, sd=sd_d, shape=n) atts = pm.Deterministic('atts', a_s - tt.mean(a_s)) defs =

问题 Hello! After running a model for a simple linear regression model in pymc3, I'm trying to do a plot. As you can see both plots show the "posterior predictive mean score", but I was wondering whether can I plot this distribution around the "posterior predictive mean score" (not around "observed score"), in arviz, similar to what is shown in the first figure? Both figures were made using the same data: Score <- c(78.93,58.2,67.47,37.47,45.65,32.92,29.97) Drugs <- c(1.17,2.97,3.26,4.69,5.83,6,6

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,

问题 I have measured the diameter of 80 fruits last year, and after checking what is the best distribution of the values, I've created a PyMC3 model with Model() as diam_model: mu = Normal('mu',mu=57,sd=5.42) sigma = Uniform('sigma',0,10) after, as far as I understand, I've "trained" the model with my prior data (the 80 values) with diam_model: dist = Normal('dist',mu=mu,sd=sigma, observed=prior_data.values) with diam_model: samples=fit().sample(1000) then I used the plot_posterior of the samples

问题 I have measured the diameter of 80 fruits last year, and after checking what is the best distribution of the values, I've created a PyMC3 model with Model() as diam_model: mu = Normal('mu',mu=57,sd=5.42) sigma = Uniform('sigma',0,10) after, as far as I understand, I've "trained" the model with my prior data (the 80 values) with diam_model: dist = Normal('dist',mu=mu,sd=sigma, observed=prior_data.values) with diam_model: samples=fit().sample(1000) then I used the plot_posterior of the samples

问题 I have measured the diameter of 80 fruits last year, and after checking what is the best distribution of the values, I've created a PyMC3 model with Model() as diam_model: mu = Normal('mu',mu=57,sd=5.42) sigma = Uniform('sigma',0,10) after, as far as I understand, I've "trained" the model with my prior data (the 80 values) with diam_model: dist = Normal('dist',mu=mu,sd=sigma, observed=prior_data.values) with diam_model: samples=fit().sample(1000) then I used the plot_posterior of the samples

来源： https://stackoverflow.com/questions/59507801/pymc3-theano-import-failure

来源： https://stackoverflow.com/questions/59507801/pymc3-theano-import-failure

来源： https://stackoverflow.com/questions/59507801/pymc3-theano-import-failure