pdf_multivariate_gauss() function in Python
Question: Which are the necessary modules for execution of the function
pdf_multivariate_gauss() in IPython?

I try to execute the below code but i get errors like "Import Error" and "Name
Error".

**_Code:_**

    
    
    import numpy as np
    from matplotlib import pyplot as plt
    from matplotlib.mlab import bivariate_normal
    import parzen_window_est
    import pdf_multivariate_gauss   ######## ImportError ########
    import operator
    
    
    from mpl_toolkits.mplot3d import Axes3D
    
    ##############################################
    ### Predicted bivariate Gaussian densities ###
    ##############################################
    
    
    mu_vec = np.array([0,0])
    cov_mat = np.array([[1,0],[0,1]])
    x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)
    # generate a range of 400 window widths between 0 < h < 1
    h_range = np.linspace(0.001, 1, 400)
    
    # calculate the actual density at the center [0, 0]
    mu = np.array([[0],[0]])
    cov = np.eye(2)
    actual_pdf_val = pdf_multivariate_gauss.pdf_multivariate_gauss(np.array([[0],[0]]), mu, cov) 
    ######## NameError #########
    
    # get a list of the differnces (|estimate-actual|) for different window widths
    parzen_estimates = [np.abs(parzen_window_est.parzen_window_est(x_2Dgauss, h=1, center=[0, 0])) for i in h_range]
    
    # get the window width for which |estimate-actual| is closest to 0
    min_index, min_value = min(enumerate(parzen_estimates), key=operator.itemgetter(1))
    

[IPython output](http://i.stack.imgur.com/uqWUp.png)


Answer: As far as I can tell, there is no such thing as pdf_multivariate_gauss (as
pointed out already). There is a python implementation of this in `scipy`,
however:
[scipy.stats.multivariate_normal](http://docs.scipy.org/doc/scipy-0.18.0/reference/generated/scipy.stats.multivariate_normal.html)

One would use it like this:

    
    
    from scipy.stats import multivariate_normal
    mvn = multivariate_normal(mu,cov) #create a multivariate Gaussian object with specified mean and covariance matrix
    p = mvn.pdf(x) #evaluate the probability density at x
    

