Note that by multiplying the output of the randn function by σ and adding m, the Gaussian random variable produced by randn now has mean m and variance σ2. We will elaborate on such transformations and others in the next chapter. Note that the MATLAB code that follows is similar to ...
Estimate the mean and variance ofW=X2+Y2+Z2 by constructing a large number of realizations of this random variable in MATLAB and then computing the sample mean and sample variance. How many samples of the random variable were needed before the sample mean and sample variance seemed to ...
w(n)=e−12(αn(L−1)/2)2=e−n2/2σ2, where –(L– 1)/2 ≤n≤ (L– 1)/2, andαis inversely proportional to the standard deviation,σ, of a Gaussian random variable. The exact correspondence with the standard deviation of a Gaussian probability density function isσ= (L–...
Load the data to the MATLAB workspace before creating the model. Create the parameter-to-matrix mapping function and log prior distribution function each as their own file. The equation-form model is a special case of the distribution-form model, with LogY = @(y,x) log(mvnpdf(y,C(x),D...
The ssm function returns an ssm object specifying the functional form and storing the parameter values of a standard linear Gaussian state-space model for a latent state process xt possibly imperfectly observed through the variable yt.
In addition, the function makes these assumptions: The variance of the complex-valued Gaussian random variable is divided equally among the real and imaginary parts. The real and imaginary parts are uncorrelated. Under these assumptions, the linear detection threshold for an NP detector is λσ=...
The pre-defined Gaussian fitting function in the Curve Fitting App is defined slightly differently than the probability distribution function of a Gaussian random variable. Since the "Results" pane in the Curve Fitting App only displays the values of the model ...
where the innovation series εt is a standard Gaussian random variable. Get ysim = log(sum(exp(XSim - mean(XSim)),2)) + thetatrue(7)*randn(T,1); To compute means of the approximate posterior smoothed state distribution, the smooth function requires response data and a model with kno...
The values in y_observed1 are noise free, and the values in y_observed2 include some random noise. Fit GPR models to the observed data sets. Get gprMdl1 = fitrgp(x_observed,y_observed1); gprMdl2 = fitrgp(x_observed,y_observed2); Compute the predicted responses and 95% prediction...
To run the example using the local MATLAB session when you have Parallel Computing Toolbox, change the global execution environment by using the mapreducer function. Get mapreducer(0) Create a datastore that references the folder location with the data. The data can be contained in a single...