- Estimate matlab. The estimate function fits an input varm model containing estimable parameters to input data. The first column is the output data of a motor-generator set (with driver) and the second column is the input data (square wave). The linearity in a linear regression model refers to the linearity of the predictor coefficients. [f,xf] = kde(a) estimates a probability density function (pdf) for the univariate data in the vector a and returns values f of the estimated pdf at the evaluation points xf. [f,xi] Improve Model Fit with Weights. Mdl = gjr(3,2) Mdl =. After writing and saving the cost function, you can use it for estimation, optimization, or sensitivity analysis at the command line. 76. ifq = instfreq(xt) estimates the instantaneous frequency of a signal stored in the MATLAB ® timetable xt. f0 = []; while ~isDone(fileReader) x = fileReader(); if VAD(x) > 0. Delays in x and y can be introduced by prepending zeros. You can compile all the estimation commands using MATLAB® Compiler™ software. Estimate Body Pose Using Deep Learning. The following tables summarize System Identification Toolbox™ commands for offline and online estimation. example F = estimateFundamentalMatrix( matchedPoints1 , matchedPoints2 ) returns the 3-by-3 fundamental matrix, F , using the least median of squares (LMedS) method from matched feature points in stereo images. histfit(r) histfit uses fitdist to fit a distribution to data. Commands for Model Estimation. Simulink® Design Optimization™ software provides both command-line tools and a graphical Parameter Estimator app for estimating parameters and states of a system modeled in Simulink. The coefficients are estimated using iterative least squares estimation, with initial values specified by beta0. 4321 7. Adjusted — R-squared adjusted for the number of coefficients. You can use this syntax for SISO and MISO systems. Matlab supports two in-built functions to compute and plot histograms: hist – introduced before R2006a histogram – introduced in R2014b. See Nonlinear Least Squares (Curve Fitting). A cost function is a MATLAB ® function that evaluates your design requirements using design variable values. It is recommended that you use timeit or tic and toc to measure the performance of your code. mdl = fitlm( ___,modelspec) defines the model specification using any of the input argument combinations in the previous syntaxes. Published: 22 Sep 2021. The estimation is based on a product Gaussian kernel function. d = finddelay(x,y) returns an estimate of the delay d between input signals x and y. Feb 2, 2021 · The MATLAB code is In this short video, we discuss parameter estimation and fit a numerical model to experimental data using MATLAB built-in solver fminunc. This approximate integration yields a value of 42. The radar is bistatic. The estimateCameraParameters function estimates extrinsic and intrinsic parameters. bode automatically determines frequencies to plot based on system dynamics. n = 0:319; x = cos(pi/4*n)+randn(size(n)); pxx = pmtm(x); example. The output, frf, is an H1 estimate computed using Welch’s method with window to window the signals. Nonlinear least-squares solves min (∑|| F ( xi ) – yi || 2 ), where F ( xi ) is a nonlinear function and yi is data. Definition. You can use the camera projection matrix, camProjection , with the findNearestNeighbors function to speed up the nearest neighbors search in a point cloud generated by an RGB-D sensor, such as Algorithms. Create a numeric vector of data. The function estimates optical flow of the input video using the method specified by the input object opticFlow. 5 s + 2. = estimateCameraParameters(imagePoints,worldPoints) returns stereoParams, a stereoParameters object containing [number,ratio] = biterr(x,y,k) also specifies k, the maximum number of bits for each element in x and y. Sep 8, 2013 · I have a two-column matrix of data obtained from NI-DAQ. fun = @(r)exp(-d*r)-y; Find the value of the optimal decay rate. VAR models are also characterized by their degree p; each equation in a VAR ( p) model contains p example. sys = nlarx(z,reg,idSigmoidNetwork(4)) sys =. load census; The vectors pop and cdate contain data for the population size and the year the census was taken, respectively. Linear model Poly2: f(x) = p1*x^2 + p2*x + p3. f ^ h ( x) = 1 n h ∑ i = 1 n K ( x − x i h) , where x1 , x2, …, xn are random samples from an unknown distribution, n is the sample size, K ( ·) is the kernel Vector Autoregression (VAR) Models. Use trapz to integrate the data with unit spacing. rng default; % For reproducibility. pd = fitdist(r, 'Normal') pd =. Feb 13, 2024 · If X is a p-by-m table or timetable, then Y = diff(X) returns a table or timetable of size (p-1)-by-m, whose elements are the differences between the rows of X. [TRANS,EMIS] = hmmestimate(seq,states) calculates the maximum likelihood estimate of the transition, TRANS , and emission, EMIS, probabilities of a hidden Markov model for sequence, seq, with known states, states. Inputs: u1, u2, u3, u4, u5, u6. For a discrete-time model, the table also includes the magnitude of each pole. Create the AR (1) model represented by this equation: y t = 1 + ϕ y t - 1 + ε t, where ε t is a series of iid Gaussian random variables with mean 0 and variance 0. The inliers are the correspondences between image points and world points that are used to compute the camera pose. damp(sys) displays the damping ratio, natural frequency, and time constant of the poles of the linear model sys. If the unsigned binary representation of any element in x or y is more than k digits, the function errors. Therefore, model constants are active model parameters to be estimated. The following example shows how to use this function in practice. If the frame contains voice activity, call pitch to estimate the fundamental frequency of the audio frame. Column 1 of ci contains the lower and upper 95% confidence interval boundaries for the mu parameter, and Obtain the multitaper PSD estimate using the default time-halfbandwidth product of 4 and DFT length. Correlations are standardized covariances, giving a dimensionless quantity that measures the degree of a linear relationship, separate from the scale of either variable. If X is a 1-by-m table or timetable, then the size of Y is 0-by-m. The parameters include camera intrinsics, distortion coefficients, and camera extrinsics. polyval(p,2) ans = 153. lsqr finds a least squares solution for x that minimizes norm(b-A*x). Mdl = arima(1,0,0); EstMdl = estimate(Mdl,Tbl1) fits the partially specified regression model with ARIMA errors Mdl to response variable and optional predictor data in the input table or timetable Tbl1, which contains time series data, and returns the fully specified, estimated regression model with ARIMA errors EstMdl . sys = tfest(tt,np) estimates the continuous-time transfer function sys with np poles, using all the input and output signals in the timetable tt. Res = table2array(mdl. Create a gjr model object using the shorthand notation gjr(P,Q), where P is the degree of the GARCH polynomial and Q is the degree of the ARCH and leverage polynomials. SampleRate, x = lsqr(A,b) attempts to solve the system of linear equations A*x = b for x using the Least Squares Method . If A is a matrix whose columns are random variables and whose rows are observations, then Description. mdl = fitlm(X,y) returns a linear regression model of the responses y, fit to the data matrix X. To specify the model order, use the general rule that the order is two times the expected number of formants plus 2. x and y must have the same number of rows. If x and y are both vectors, they must have the same length. f=fit(cdate,pop, 'poly2') f =. Store the results in a vector of estimated varm objects. R a d j 2 = 1 − ( n − 1 n reg = linearRegressor(names,lags); Estimate a nonlinear ARX model using an idSigmoidNetwork mapping function with four units for all output channels. Load the census sample data set. 9884. Zisserman. A 100(1 – α)% confidence interval gives the range for the corresponding regression coefficient with 100(1 – α)% confidence, meaning that 100(1 – α)% of the intervals resulting from repeated experimentation will contain the true value of the coefficient. [pdca,gn,gl] = fitdist(x,distname,'By',groupvar) creates probability Calculate the integral of a vector where the spacing between data points is 1. If one of the signals is a matrix and the other is a vector, then the length of the vector must equal the number of rows in the Estimate Transfer Function Model. gjr with properties: Description: "GJR(3,2) Conditional Variance Model (Gaussian Distribution)" SeriesName: "Y". "MLESAC: A New Robust Estimator with Application to Estimating Image Cross-correlation measures the similarity between a vector x and shifted (lagged) copies of a vector y as a function of the lag. r = normrnd(10,1,100,1); Construct a histogram with a normal distribution fit. The state Jacobian replaces the state transition matrix. r = xcorr(x) returns the autocorrelation sequence of x. For example, the Constant property is a 2-by-1 vector of NaN values. In the frequency range, [0,|Fs|/2], you expect three formants. The following MATLAB ® functions compute sample correlation coefficients and covariance. Therefore, set the model order equal to 8. A and B must be the same size. Generate sample data from different normal distributions by using the randn function. For multiple-output AR models only, specify the model-order matrix in the MATLAB ® workspace before estimation, as described in Polynomial Sizes and Orders of Multi-Output Polynomial Models. The values of s are determined by cubic spline interpolation of x and y. sys is an idss model of the following form: x ˙ ( t) = A x C = cov(A,B) returns the covariance between two random variables A and B. MATLAB® displays a table containing an estimation summary, which includes parameter estimates and inferences. I want to The Beta property is an empty matrix; MATLAB® populates Beta during estimation when you specify predictor data. Estimate all VARX models in the vector. Alternatively, you can evaluate a polynomial in a matrix sense using polyvalm. This example shows how to obtain equivalent nonparametric power spectral density (PSD) estimates using the periodogram and fft functions. where wi are the weights. See Linear Least Squares. The number of zeros in sys is max ( np -1,0). For example, you can indicate censored data or specify control parameters for the iterative fitting algorithm. Extended Kalman Filter Loop. To examine the estimation objective function in more detail, type edit sdoAircraftEstimation_Objective at the MATLAB command prompt. x = grades(:,1); Fit a normal distribution to the sample data by using fitdist to create a probability distribution object. Kernel Density Estimator. The target is located 50 km from the transmitter and 75 km from the receiver. A vector autoregression (VAR) model is a multivariate time series model containing a system of n equations of n distinct, stationary response variables as linear functions of lagged responses and other terms. The ss model object can represent SISO or MIMO state-space models in continuous time or discrete time. tic/toc and timeit. Description. Least Squares. In continuous-time, a state-space model is of the following form: x ˙ = A x + B u y = C x + D u. Typically, if we have a vector of random numbers that is drawn from a distribution, we can estimate the PDF using the histogram tool. Y = diff(X,n) calculates the nth difference by applying the diff(X) operator recursively n times. Sep 22, 2021 · How to Estimate Model Parameters from Test Data with Simulink. The polynomial expression in one variable, p ( x) = 4 x 5 - 3 x 2 + 2 x + 3 3 Oct 6, 2016 · Step 2: Plot the estimated histogram. ci = paramci(pd) ci = 2×2. The transmitter and receiver are collocated isotropic antenna elements forming a monostatic radar system. For a signal xn sampled at fs samples per unit time, the periodogram is defined as. The MATLAB online version I'm using has a maximum array size of 5. Specify estimation data, progress plots, and optimization methods; speed up estimation using parallel computing and fast restart. The estimate is based on a normal kernel function, and is evaluated at equally-spaced points, xi, that cover the range of the data in x. The function bandpower allows you to estimate signal power in one step. 5046. The intervals next to the parameter estimates are the 95% confidence intervals for the distribution parameters. From the series: “How To” Video Series for Biomedical and Pharmaceutical Applications. For this example, use a continuous-time transfer function: s y s = s 2 + 5 s + 5 s 4 + 1. V = var(A) returns the variance of the elements of A along the first array dimension whose size is greater than 1. ksdensity works best with continuously distributed samples. b = regress(y,X) returns a vector b of coefficient estimates for a multiple linear regression of the responses in vector y on the predictors in matrix X. Use fitdist to obtain parameters used in fitting. The transmitter has a peak transmit power of 1 MW with a gain of 40 dB. Y = [1 4 9 16 25]; Y contains function values for f ( x) = x 2 in the domain [1, 5]. When the attempt is successful, lsqr displays a message to confirm convergence. This action opens a new session with the name Parameter Estimation - spe_engine_throttle1 in the Parameter Estimator. [f,xf,bw] = kde(a) also returns the bandwidth for The function also returns the images you used to estimate the camera parameters and the standard estimation errors for the single camera calibration. hmmestimate(,'Symbols',SYMBOLS) specifies the symbols that are emitted. x0 = 4; x = lsqnonlin(fun,x0) The coefficient confidence intervals provide a measure of precision for regression coefficient estimates. This appears to mean that there is about 22 MB of data to bode(sys) creates a Bode plot of the frequency response of a dynamic system model sys. SYMBOLS can be a numeric array, a string sys = ssest(tt,nx) estimates the continuous-time state-space model sys of order nx, using all the input and output signals in the timetable tt. You can estimate the states of your system using real-time data and linear and nonlinear Kalman filter algorithms. Online state estimation algorithms update state estimates of your system when new data is available. load examgrades. The radar operating frequency is 10. Note: This example uses regress , which is useful when you simply need the coefficient estimates or residuals of a regression model and you need to repeat fitting a model multiple times, as in the case of bootstrapping. The receiver gain is 20 dB. R 2 = S S R S S T = 1 − S S E S S T. Generate a sample of size 100 from a normal distribution with mean 10 and variance 1. pp = spline(x,y) returns a piecewise polynomial structure for use by ppval and the spline utility unmkpp. Q = trapz(Y) Q = 42. For standalone C/C++ code, by default, the code generator produces code for FFT algorithms instead of producing FFT Camera calibration is the process of estimating camera parameters by using images that contain a calibration pattern. 01,'Options',Opt instructs mle to estimate the parameters for the distribution of censored data specified by the array Cens, compute the 99% confidence limits for the parameter estimates, and use the algorithm control parameters specified by the structure Opt. Residuals); boxplot(Res) You can see the right-skewed structure of the residuals in the box plot as well. r = k*randn([20,1]) + (1/20)*(k^3); rnorm = [rnorm;r]; The corresponding spectral estimate is normalized, as described in Spectrum Normalization and differs from the spectrum normalization in the Signal Processing Toolbox™ product. Then use the phased. If A and B are vectors of observations with equal length, cov(A,B) is the 2 -by- 2 covariance matrix. Find the roots of the prediction polynomial returned by lpc. Create the transfer function and examine its step response. 0 GHz. Plot the normal probability plot of the raw residuals. The different cases show you how to properly scale the output of fft for even-length inputs, for normalized frequencies and frequencies in hertz, and for one- and two-sided PSD estimates. Use the weighted least-squares fitting method if the weights are known, or if the weights follow a particular form. txy = tfestimate(x,y) finds a transfer function estimate between the input signal x and the output signal y evaluated at a set of frequencies. A regression model describes the relationship between a response and predictors. This example shows how to estimate the body pose of one or more people using the OpenPose algorithm and a pretrained network. As a result, we obtain that the estimated variance of is 1. [acf,lags] = autocorr(y) returns the sample autocorrelation function (ACF) and associated lags of the input univariate time series. Linear least-squares solves min|| C * x - d || 2, possibly with bounds or linear constraints. These functions return wall-clock time. estimate selects the response variable When you use robust estimation, results may not be identical between runs because of the randomized nature of the algorithm. Get. Estimate the output SNR for a target with an RCS of 1 m². ( root ( z 3 + z 2 + a, z, 1) root ( z 3 + z 2 + a, z, 2) root ( z 3 + z 2 + a, z, 3)) Try to get an explicit solution for such equations by calling the solver with 'MaxDegree'. kde uses kernel density estimation to estimate the pdf. These sample coefficients are estimates of the true covariance and correlation Numerical Integration and Differentiation. The poles are sorted in increasing order of frequency values. The function eliminates spurious outlier correspondences using the M-estimator sample consensus (MSAC) algorithm. R-squared is the proportion of the total sum of squares explained by the model. and A. Consider a unit chirp embedded in white Gaussian noise and sampled at 1 kHz for 1. For detailed information about using each command, see the corresponding reference page. MATLAB® considers any NaN values as unknown parameter values to be estimated. The optical flow is estimated as the motion between two consecutive video frames. Create a vector containing the first column of exam grade data. The sample data contains a 120-by-5 matrix of exam grades. Create an GJR (3,2) model. Plot the box plot of all four types of residuals. For limitations related to variable-size data, see Variable-Sizing Restrictions for Code Generation of Toolbox Functions (MATLAB Coder). Use the properties of a LinearModel object to investigate a fitted linear regression model. The default number of DFT points is 512. 28125 KB or 5098. If sys is a multi-input, multi-output (MIMO) model, then bode mdl = fitlm(tbl,y) uses the variables in tbl for the predictors and y for the response. To compute coefficient estimates for a model with a constant term (intercept), include a column of ones in the matrix X. pd = fitdist(x, 'Normal') Jan 17, 2023 · To calculate MSE in MATLAB, we can use the mse(X, Y) function. solve(eqn, x) ans = . ifq = instfreq(x,t) estimates the instantaneous frequency of x sampled at the time values stored in t. RangeDopplerResponse System object™. plotResiduals(mdl) The histogram shows that the residuals are slightly right skewed. If the camera intrinsics and extrinsics parameters are known, you can estimate the camera projection matrix using the cameraProjection function. Because the signal is real-valued, the PSD estimate is one-sided and there are 512/2+1 points in the PSD estimate. 0 GB, but if I increase the array to 25851 by 25851, it says I exceed the 5. All the MATLAB codes presented in this lecture are stored in a zipped file, which you can download. Use these camera parameters to remove lens distortion effects from an image, measure planar objects, reconstruct 3-D scenes from multiple Description. The power of a signal is the sum of the absolute squares of its time-domain samples divided by the signal length, or, equivalently, the square of its RMS level. Compute step-response characteristics, such as rise time, settling time, and overshoot, for a dynamic system model. MATLAB files. Create an anonymous function that takes a value of the exponential decay rate r and returns a vector of differences from the model with that decay rate and the data. The cputime function measures the total CPU time This MATLAB function returns the maximum likelihood estimates of the parameter of the Rayleigh distribution given the data in the vector data. Example: 'Censoring',Cens,'Alpha',0. The goal of body pose estimation is to identify the location of people in an image and the orientation of their body parts. The input points can be M -by-2 matrices of M number of [ x , y ] coordinates, or a KAZEPoints , SIFTPoints , SURFPoints , MSERRegions , BRISKPoints , or cornerPoints object. 5846 9. For an example, see Example: Using Basic Fitting UI . The periodogram is the Fourier transform of the biased estimate of the autocorrelation sequence. If x and y have different lengths, the function appends zeros to the end of the shorter vector so it has the same length as the other. s = spline(x,y,xq) returns a vector of interpolated values s corresponding to the query points in xq. If one of the signals is a matrix and the other is a vector, then the length of the vector must equal the number of rows in the matrix. Rsquared, a property of the fitted model, is a structure with two fields: Ordinary — Ordinary (unadjusted) R-squared. Fit Models to Data. frf = modalfrf(x,y,fs,window) estimates a matrix of frequency response functions, frf , from the excitation signals, x, and the response signals, y, all sampled at a rate fs. The plot displays the magnitude (in dB) and phase (in degrees) of the system response as a function of frequency. A = lpc(x1,8); rts = roots(A); It serves as a template for model estimation. Least squares problems have two types. pd = fitdist(x,distname,Name,Value) creates the probability distribution object with additional options specified by one or more name-value pair arguments. Arbitrarily choose an initial guess x0 = 4. Copy Command. When you optimize or estimate model parameters, you provide the saved cost For more details on how to write an objective/constraint function to use with the sdo. This can be translated into an estimate of the variance of with the Delta method, by multiplying the estimated variance of by . 2 seconds. Estimate Parameters and States. When you know how to evaluate the function, you can use integral to calculate integrals with specified bounds. This example shows how to fit a polynomial model to data using both the linear least-squares method and the weighted least-squares method for comparison. To estimate the range and speed of three targets, create a range-Doppler map using the phased. SeriesName or the sole variable in Estimate model order, as described in Preliminary Step – Estimating Model Orders and Input Delays. optimize command, type help sdoExampleCostFunction at the MATLAB® command prompt. For MEX output, MATLAB ® Coder™ uses the library that MATLAB uses for FFT algorithms. [b,bint] = regress(y,X) also returns a matrix bint of 95% The cputime Function vs. The exams are scored on a scale of 0 to 100. For univariate or bivariate data, use ksdensity instead. Unlike tic and toc , the timeit function calls your code multiple times, and, therefore, considers first-time costs. For example, the Value column contains corresponding maximum-likelihood estimates, and the PValue column contains p -values for the asymptotic t -test of the null hypothesis that the corresponding parameter is 0. 5. 6 5 s 3 + 5 s 2 + 6. 99 decision = pitch(x,fileReader. DopplerEstimator System objects to estimate range and speed. The default value is 2. If A and B are matrices of observations, cov(A,B) treats A and B as vectors and is equivalent to cov(A(:),B(:)). 9797 GB in size. If A is a vector of observations, then V is a scalar. To start the estimation process, first open the Parameter Estimator app from the Simulink model. When multiple people are present in a scene, pose After entering the polynomial into MATLAB® as a vector, use the polyval function to evaluate the polynomial at a specific value. 0 GB limit even though the new array is only 4. To integrate an array of data where the underlying equation is unknown, you can use trapz, which performs trapezoidal integration using the data points to form a series of trapezoids with Jun 18, 2013 · The array will need 5220488. By default, the variance is normalized by N-1 , where N is the number of observations. RangeEstimator and phased. When A is consistent, the least squares solution is also a solution of the linear system. 7391. This MATLAB function fits a model to noisy data using the M-estimator sample consensus (MSAC) algorithm, a version of the random sample consensus (RANSAC) algorithm. ACFTbl = autocorr(Tbl) returns a table containing variables for the sample ACF and associated lags of the last variable in the input table or timetable. The pulse width is 1 μs. Use the shorthand syntax to specify an AR (1) model template, then use dot notation to set the Constant and Variance properties. Use polyval to evaluate p ( 2). You also can use the MATLAB polyfit and polyval functions to fit your data to a model that is linear in the coefficients. You can perform online state estimation using Simulink ® blocks, generate C/C++ code for these blocks using Simulink Coder EstMdl = estimate(Mdl,Tbl1) fits the partially specified conditional variance model Mdl to response variable in the input table or timetable Tbl1 , which contains time series data, and returns the fully specified, estimated conditional variance model EstMdl . The extended Kalman filter loop is almost identical to the loop of Linear Kalman Filters except that: The filter uses the exact nonlinear state update and measurement functions whenever possible. example. Using MATLAB Coder™ software, you LinearModel is a fitted linear regression model object. The weighted least-squares fitting method introduces weights in the formula for the SSE, which becomes. If the frame does not contain voice activity, declare the fundamental frequency as NaN . 73. The function uses persistence-of-excitation analysis on the input data to select the model order (number of nonzero impulse response coefficients). The kernel density estimator is the estimated pdf of a random variable. 13309 MB or 4. The video frame T at the given instant tcurrent is referred as current frame and the video frame T-1 is referred as previous frame. How to cite E = estimateEssentialMatrix(matchedPoints1,matchedPoints2,intrinsics) returns the 3-by-3 essential matrix, E, using the M-estimator sample consensus (MSAC) algorithm. Fit a quadratic curve to the population data. estimate selects the response variable named in Mdl. The measurement Jacobian replaces the measurement matrix. The MATLAB ® Basic Fitting UI helps you to fit your data, so you can calculate model coefficients and plot the model on top of the data. In the Simulink Toolstrip, on the Apps tab, under Control Systems, select Parameter Estimator. S S E = ∑ i = 1 n w i ( y i − y ^ i) 2. Suppose we have the following two arrays in MATLAB that show the actual values and the forecasted values for some model: This MATLAB function returns a 2-D geometric transform object, tform. beta = nlinfit(X,Y,modelfun,beta0) returns a vector of estimated coefficients for the nonlinear regression of the responses in Y on the predictors in X using the model specified by modelfun. = damp(sys) returns the natural frequencies wn, and damping ratios zeta of the poles of sys. (since R2023a) example. ksdensity estimates the density at 100 points for univariate data, or 900 points for bivariate data. The function assumes that the last variable in the timetable is the single output signal. The function treats all variables in the timetable and all columns inside each variable independently. For any real values of x, the kernel density estimator's formula is given by. Here, x, u and y represent the states, inputs and outputs respectively, while A , B, C and D are the state-space matrices. . If data is a timetable, the software interprets the last variable is the single output variable. To select a different variable for which to You can select specific input and output channels to use for estimation by specifying the channel names in the InputName and OutputName name-value arguments. Example: How to Calculate MSE in MATLAB. Learn how to improve your Simulink ® model accuracy by automatically estimating model parameters from test data in just a few clicks. Estimate the standard errors for a coefficient vector in a linear regression by bootstrapping the residuals. d = finddelay(x,y,maxlag) uses maxlag to find the estimated delay (s) between x and y. for k=1:20. Nonlinear ARX model with 2 outputs and 6 inputs. See Kernel Distribution for more information. Write a Cost Function. The ss object The periodogram is a nonparametric estimate of the power spectral density (PSD) of a wide-sense stationary random process. 97865 GB of memory. You can also obtain these intervals by using the function paramci. The option specifies the maximum degree of polynomials for which the solver tries to return explicit solutions. f = mvksdensity(x,pts,'Bandwidth',bw,Name,Value) returns any of the previous output arguments, using additional options specified by one or more Name,Value pair arguments. uc eh ps tj bu tb ce xo jf xx