# estimate

Estimate posterior distribution of Bayesian linear regression model parameters

## Description

To perform predictor variable selection for a Bayesian linear regression model, see estimate.

example

PosteriorMdl = estimate(PriorMdl,X,y) returns the Bayesian linear regression model PosteriorMdl that characterizes the joint posterior distributions of the coefficients β and the disturbance variance σ2. PriorMdl specifies the joint prior distribution of the parameters and the structure of the linear regression model. X is the predictor data and y is the response data. PriorMdl and PosteriorMdl might not be the same object type.

To produce PosteriorMdl, the estimate function updates the prior distribution with information about the parameters that it obtains from the data.

NaNs in the data indicate missing values, which estimate removes by using list-wise deletion.

example

PosteriorMdl = estimate(PriorMdl,X,y,Name,Value) specifies additional options using one or more name-value pair arguments. For example, you can specify a value for either β or σ2 to estimate the conditional posterior distribution of one parameter given the specified value of the other parameter.

If you specify the Beta or Sigma2 name-value pair argument, then PosteriorMdl and PriorMdl are equal.

example

[PosteriorMdl,Summary] = estimate(___) uses any of the input argument combinations in the previous syntaxes to return a table that contains the following for each parameter: the posterior mean and standard deviation, 95% credible interval, posterior probability that the parameter is greater than 0, and description of the posterior distribution (if one exists). Also, the table contains the posterior covariance matrix of β and σ2. If you specify the Beta or Sigma2 name-value pair argument, then estimate returns conditional posterior estimates.

## Examples

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Consider a model that predicts the fuel economy (in MPG) of a car given its engine displacement and weight.

x = [Displacement Weight];
y = MPG;

Regress fuel economy onto engine displacement and weight, including an intercept to obtain ordinary least-squares (OLS) estimates.

Mdl = fitlm(x,y)
Mdl =
Linear regression model:
y ~ 1 + x1 + x2

Estimated Coefficients:
Estimate        SE         tStat       pValue
__________    _________    _______    __________

(Intercept)        46.925       2.0858     22.497    6.0509e-39
x1              -0.014593    0.0082695    -1.7647      0.080968
x2             -0.0068422    0.0011337    -6.0353    3.3838e-08

Number of observations: 94, Error degrees of freedom: 91
Root Mean Squared Error: 4.09
F-statistic vs. constant model: 134, p-value = 7.22e-28
Mdl.MSE
ans = 16.7100

Create a default, diffuse prior distribution for one predictor.

p = 2;
PriorMdl = bayeslm(p);

PriorMdl is a diffuseblm model object.

Use default options to estimate the posterior distribution.

PosteriorMdl = estimate(PriorMdl,x,y);
Method: Analytic posterior distributions
Number of observations: 94
Number of predictors:   3

|   Mean     Std         CI95        Positive       Distribution
--------------------------------------------------------------------------------
Intercept | 46.9247  2.1091  [42.782, 51.068]    1.000   t (46.92, 2.09^2, 91)
Beta(1)   | -0.0146  0.0084  [-0.031,  0.002]    0.040   t (-0.01, 0.01^2, 91)
Beta(2)   | -0.0068  0.0011  [-0.009, -0.005]    0.000   t (-0.01, 0.00^2, 91)
Sigma2    | 17.0855  2.5905  [12.748, 22.866]    1.000   IG(45.50, 0.0013)

PosteriorMdl is a conjugateblm model object.

The posterior means and the OLS coefficient estimates are almost identical. Also, the posterior standard deviations and OLS standard errors are almost identical. The posterior mean of Sigma2 is close to the OLS mean squared error (MSE).

Consider the multiple linear regression model that predicts the US real gross national product (GNPR) using a linear combination of total employment (E) and real wages (WR).

For all , is a series of independent Gaussian disturbances with a mean of 0 and variance . Assume these prior distributions:

• is a 3-D t distribution with 10 degrees of freedom for each component, correlation matrix C, location ct, and scale st.

• , with shape and scale .

bayeslm treats these assumptions and the data likelihood as if the corresponding posterior is analytically intractable.

Declare a MATLAB® function that:

• Accepts values of and together in a column vector, and accepts values of the hyperparameters

• Returns the value of the joint prior distribution, , given the values of and

function logPDF = priorMVTIG(params,ct,st,dof,C,a,b)
%priorMVTIG Log density of multivariate t times inverse gamma
%   priorMVTIG passes params(1:end-1) to the multivariate t density
%   function with dof degrees of freedom for each component and positive
%   definite correlation matrix C. priorMVTIG returns the log of the product of
%   the two evaluated densities.
%
%   params: Parameter values at which the densities are evaluated, an
%           m-by-1 numeric vector.
%
%       ct: Multivariate t distribution component centers, an (m-1)-by-1
%           numeric vector.  Elements correspond to the first m-1 elements
%           of params.
%
%       st: Multivariate t distribution component scales, an (m-1)-by-1
%           numeric (m-1)-by-1 numeric vector.  Elements correspond to the
%           first m-1 elements of params.
%
%      dof: Degrees of freedom for the multivariate t distribution, a
%           numeric scalar or (m-1)-by-1 numeric vector. priorMVTIG expands
%           scalars such that dof = dof*ones(m-1,1). Elements of dof
%           correspond to the elements of params(1:end-1).
%
%        C: Correlation matrix for the multivariate t distribution, an
%           (m-1)-by-(m-1) symmetric, positive definite matrix. Rows and
%           columns correspond to the elements of params(1:end-1).
%
%        a: Inverse gamma shape parameter, a positive numeric scalar.
%
%        b: Inverse gamma scale parameter, a positive scalar.
%
beta = params(1:(end-1));
sigma2 = params(end);

tVal = (beta - ct)./st;
mvtDensity = mvtpdf(tVal,C,dof);
igDensity = sigma2^(-a-1)*exp(-1/(sigma2*b))/(gamma(a)*b^a);

logPDF = log(mvtDensity*igDensity);
end

Create an anonymous function that operates like priorMVTIG, but accepts the parameter values only, and holds the hyperparameter values fixed at arbitrarily chosen values.

prednames = ["E" "WR"];
p = numel(prednames);
numcoeff = p + 1;

rng(1); % For reproducibility
dof = 10;
V = rand(numcoeff);
Sigma = 0.5*(V + V') + numcoeff*eye(numcoeff);
st = sqrt(diag(Sigma));
C = diag(1./st)*Sigma*diag(1./st);
ct = rand(numcoeff,1);
a = 10*rand;
b = 10*rand;
logPDF = @(params)priorMVTIG(params,ct,st,dof,C,a,b);

Create a custom joint prior model for the linear regression parameters. Specify the number of predictors p. Also, specify the function handle for priorMVTIG and the variable names.

PriorMdl = bayeslm(p,'ModelType','custom','LogPDF',logPDF,...
'VarNames',prednames);

PriorMdl is a customblm Bayesian linear regression model object representing the prior distribution of the regression coefficients and disturbance variance.

Load the Nelson-Plosser data set. Create variables for the response and predictor series.

X = DataTable{:,PriorMdl.VarNames(2:end)};
y = DataTable{:,"GNPR"};

Estimate the marginal posterior distributions of and using the Hamiltonian Monte Carlo (HMC) sampler. Specify drawing 10,000 samples and a burn-in period of 1000 draws.

PosteriorMdl = estimate(PriorMdl,X,y,'Sampler','hmc','NumDraws',1e4,...
'Burnin',1e3);
Method: MCMC sampling with 10000 draws
Number of observations: 62
Number of predictors:   3

|    Mean       Std            CI95         Positive  Distribution
------------------------------------------------------------------------------
Intercept |   -3.6449    5.6075  [-15.871,  6.226]      0.255     Empirical
E         |   -0.0056    0.0006   [-0.007, -0.004]      0.000     Empirical
WR        |   15.2494    0.7724   [13.729, 16.762]      1.000     Empirical
Sigma2    | 1289.1139  244.8320  [900.424, 1858.915]    1.000     Empirical

PosteriorMdl is an empiricalblm model object storing the draws from the posterior distributions.

View a trace plot and an ACF plot of the draws from the posterior of (for example) and the disturbance variance. Do not plot the burn-in period.

figure;
subplot(2,1,1)
title(['Trace Plot ' char(8212) ' \beta_1']);
xlabel('MCMC Draw')
ylabel('Simulation Index')
subplot(2,1,2)

figure;
subplot(2,1,1)
plot(PosteriorMdl.Sigma2Draws(1001:end));
title(['Trace Plot ' char(8212) ' Disturbance Variance']);
xlabel('MCMC Draw')
ylabel('Simulation Index')
subplot(2,1,2)
autocorr(PosteriorMdl.Sigma2Draws(1001:end))

The MCMC sample of the disturbance variance appears to mix well.

Consider the regression model in Estimate Posterior Using Hamiltonian Monte Carlo Sampler. This example uses the same data and context, but assumes a diffuse prior model instead.

Create a diffuse prior model for the linear regression parameters. Specify the number of predictors p and the names of the regression coefficients.

p = 3;
PriorMdl = bayeslm(p,'ModelType','diffuse','VarNames',["IPI" "E" "WR"])
PriorMdl =
diffuseblm with properties:

NumPredictors: 3
Intercept: 1
VarNames: {4x1 cell}

| Mean  Std        CI95        Positive        Distribution
-----------------------------------------------------------------------------
Intercept |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
IPI       |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
E         |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
WR        |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
Sigma2    |  Inf  Inf  [   NaN,    NaN]    1.000   Proportional to 1/Sigma2

PriorMdl is a diffuseblm model object.

Load the Nelson-Plosser data set. Create variables for the response and predictor series.

X = DataTable{:,PriorMdl.VarNames(2:end)};
y = DataTable{:,'GNPR'};

Estimate the conditional posterior distribution of $\beta$ given the data and that ${\sigma }^{2}=2$, and return the estimation summary table to access the estimates.

[Mdl,SummaryBeta] = estimate(PriorMdl,X,y,'Sigma2',2);
Method: Analytic posterior distributions
Conditional variable: Sigma2 fixed at   2
Number of observations: 62
Number of predictors:   4

|   Mean      Std          CI95         Positive     Distribution
--------------------------------------------------------------------------------
Intercept | -24.2536  1.8696  [-27.918, -20.589]    0.000   N (-24.25, 1.87^2)
IPI       |   4.3913  0.0301   [ 4.332,  4.450]     1.000   N (4.39, 0.03^2)
E         |   0.0011  0.0001   [ 0.001,  0.001]     1.000   N (0.00, 0.00^2)
WR        |   2.4682  0.0743   [ 2.323,  2.614]     1.000   N (2.47, 0.07^2)
Sigma2    |    2       0       [ 2.000,  2.000]     1.000   Fixed value

estimate displays a summary of the conditional posterior distribution of $\beta$. Because ${\sigma }^{2}$ is fixed at 2 during estimation, inferences on it are trivial.

Extract the mean vector and covariance matrix of the conditional posterior of $\beta$ from the estimation summary table.

condPostMeanBeta = SummaryBeta.Mean(1:(end - 1))
condPostMeanBeta = 4×1

-24.2536
4.3913
0.0011
2.4682

CondPostCovBeta = SummaryBeta.Covariances(1:(end - 1),1:(end - 1))
CondPostCovBeta = 4×4

3.4956    0.0350   -0.0001    0.0241
0.0350    0.0009   -0.0000   -0.0013
-0.0001   -0.0000    0.0000   -0.0000
0.0241   -0.0013   -0.0000    0.0055

Display Mdl.

Mdl
Mdl =
diffuseblm with properties:

NumPredictors: 3
Intercept: 1
VarNames: {4x1 cell}

| Mean  Std        CI95        Positive        Distribution
-----------------------------------------------------------------------------
Intercept |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
IPI       |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
E         |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
WR        |  0    Inf  [   NaN,    NaN]    0.500   Proportional to one
Sigma2    |  Inf  Inf  [   NaN,    NaN]    1.000   Proportional to 1/Sigma2

Because estimate computes the conditional posterior distribution, it returns the original prior model, not the posterior, in the first position of the output argument list.

Estimate the conditional posterior distributions of ${\sigma }^{2}$ given that $\beta$ is condPostMeanBeta.

[~,SummarySigma2] = estimate(PriorMdl,X,y,'Beta',condPostMeanBeta);
Method: Analytic posterior distributions
Conditional variable: Beta fixed at -24.2536       4.3913   0.00112035      2.46823
Number of observations: 62
Number of predictors:   4

|   Mean      Std          CI95         Positive     Distribution
--------------------------------------------------------------------------------
Intercept | -24.2536   0      [-24.254, -24.254]    0.000   Fixed value
IPI       |   4.3913   0       [ 4.391,  4.391]     1.000   Fixed value
E         |   0.0011   0       [ 0.001,  0.001]     1.000   Fixed value
WR        |   2.4682   0       [ 2.468,  2.468]     1.000   Fixed value
Sigma2    |  48.5138  9.0088   [33.984, 69.098]     1.000   IG(31.00, 0.00069)

estimate displays a summary of the conditional posterior distribution of ${\sigma }^{2}$. Because $\beta$ is fixed to condPostMeanBeta during estimation, inferences on it are trivial.

Extract the mean and variance of the conditional posterior of ${\sigma }^{2}$ from the estimation summary table.

condPostMeanSigma2 = SummarySigma2.Mean(end)
condPostMeanSigma2 = 48.5138
CondPostVarSigma2 = SummarySigma2.Covariances(end,end)
CondPostVarSigma2 = 81.1581

Consider the regression model in Estimate Posterior Using Hamiltonian Monte Carlo Sampler. This example uses the same data and context, but assumes a semiconjugate prior model instead.

Create a semiconjugate prior model for the linear regression parameters. Specify the number of predictors p and the names of the regression coefficients.

p = 3;
PriorMdl = bayeslm(p,'ModelType','semiconjugate',...
'VarNames',["IPI" "E" "WR"]);

PriorMdl is a semiconjugateblm model object.

Load the Nelson-Plosser data set. Create variables for the response and predictor series.

X = DataTable{:,PriorMdl.VarNames(2:end)};
y = DataTable{:,'GNPR'};

Estimate the marginal posterior distributions of $\beta$ and ${\sigma }^{2}$.

rng(1); % For reproducibility
[PosteriorMdl,Summary] = estimate(PriorMdl,X,y);
Method: Gibbs sampling with 10000 draws
Number of observations: 62
Number of predictors:   4

|   Mean      Std          CI95        Positive  Distribution
-------------------------------------------------------------------------
Intercept | -23.9922  9.0520  [-41.734, -6.198]    0.005     Empirical
IPI       |   4.3929  0.1458   [ 4.101,  4.678]    1.000     Empirical
E         |   0.0011  0.0003   [ 0.000,  0.002]    0.999     Empirical
WR        |   2.4711  0.3576   [ 1.762,  3.178]    1.000     Empirical
Sigma2    |  46.7474  8.4550   [33.099, 66.126]    1.000     Empirical

PosteriorMdl is an empiricalblm model object because marginal posterior distributions of semiconjugate models are analytically intractable, so estimate must implement a Gibbs sampler. Summary is a table containing the estimates and inferences that estimate displays at the command line.

Display the summary table.

Summary
Summary=5×6 table
Mean          Std                  CI95              Positive    Distribution                                  Covariances
_________    __________    ________________________    ________    _____________    ______________________________________________________________________

Intercept      -23.992         9.052       -41.734       -6.1976     0.0053     {'Empirical'}        81.938        0.81622     -0.0025308        0.58928              0
IPI             4.3929       0.14578        4.1011        4.6782          1     {'Empirical'}       0.81622       0.021252    -7.1663e-06      -0.030939              0
E            0.0011124    0.00033976    0.00045128     0.0017883     0.9989     {'Empirical'}    -0.0025308    -7.1663e-06     1.1544e-07    -8.4598e-05              0
WR              2.4711        0.3576        1.7622        3.1781          1     {'Empirical'}       0.58928      -0.030939    -8.4598e-05        0.12788              0
Sigma2          46.747         8.455        33.099        66.126          1     {'Empirical'}             0              0              0              0         71.487

Access the 95% equitailed credible interval of the regression coefficient of IPI.

Summary.CI95(2,:)
ans = 1×2

4.1011    4.6782

## Input Arguments

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Bayesian linear regression model representing a prior model, specified as an object in this table.

Model ObjectDescription
conjugateblmDependent, normal-inverse-gamma conjugate model returned by bayeslm or estimate
semiconjugateblmIndependent, normal-inverse-gamma semiconjugate model returned by bayeslm
diffuseblmDiffuse prior model returned by bayeslm
empiricalblmPrior model characterized by samples from prior distributions, returned by bayeslm or estimate
customblmPrior distribution function that you declare returned by bayeslm

PriorMdl can also represent a joint posterior model returned by estimate, either a conjugateblm or empiricalblm model object. In this case, estimate updates the joint posterior distribution using the new observations in X and y.

Predictor data for the multiple linear regression model, specified as a numObservations-by-PriorMdl.NumPredictors numeric matrix. numObservations is the number of observations and must be equal to the length of y.

Data Types: double

Response data for the multiple linear regression model, specified as a numeric vector with numObservations elements.

Data Types: double

### Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'Sigma2',2 specifies estimating the conditional posterior distribution of the regression coefficients given the data and that the specified disturbance variance is 2.
Options for All Prior Distributions

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Flag to display Bayesian estimator summary at the command line, specified as the comma-separated pair consisting of 'Display' and a value in this table.

ValueDescription
trueestimate prints estimation information and a table summarizing the Bayesian estimators to the command line.
falseestimate does not print to the command line.

The estimation information includes the estimation method, fixed parameters, the number of observations, and the number of predictors. The summary table contains estimated posterior means and standard deviations (square root of the posterior variance), 95% equitailed credible intervals, the posterior probability that the parameter is greater than 0, and a description of the posterior distribution (if known).

If you specify one of Beta or Sigma2, then estimate includes your specification in the display, and corresponding posterior estimates are trivial.

Example: 'Display',false

Data Types: logical

Options for All Prior Distributions Except Empirical

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Value of the regression coefficients for conditional posterior distribution estimation of the disturbance variance, specified as the comma-separated pair consisting of 'Beta' and a (PriorMdl.Intercept + PriorMdl.NumPredictors)-by-1 numeric vector. estimate estimates the characteristics of π(σ2|y,X,β = Beta), where y is y, X is X, and Beta is the value of 'Beta'. If PriorMdl.Intercept is true, then Beta(1) corresponds to the model intercept. All other values correspond to the predictor variables that compose the columns of X. Beta cannot contain any NaN values (that is, all coefficients must be known).

You cannot specify Beta and Sigma2 simultaneously.

By default, estimate does not compute characteristics of the conditional posterior of σ2.

Example: 'Beta',1:3

Data Types: double

Value of the disturbance variance for conditional posterior distribution estimation of the regression coefficients, specified as the comma-separated pair consisting of 'Sigma2' and a positive numeric scalar. estimate estimates characteristics of π(β|y,X,Sigma2), where y is y, X is X, and Sigma2 is the value of 'Sigma2'.

You cannot specify Sigma2 and Beta simultaneously.

By default, estimate does not compute characteristics of the conditional posterior of β.

Example: 'Sigma2',1

Data Types: double

Options for Semiconjugate, Empirical, and Custom Prior Distributions

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Monte Carlo simulation adjusted sample size, specified as the comma-separated pair consisting of 'NumDraws' and a positive integer. estimate actually draws BurnIn + NumDraws*Thin samples, but it bases the estimates off NumDraws samples. For details on how estimate reduces the full Monte Carlo sample, see Algorithms.

If PriorMdl is a semiconjugateblm model and you specify Beta or Sigma2, then MATLAB® ignores NumDraws.

Example: 'NumDraws',1e7

Data Types: double

Options for Semiconjugate and Custom Prior Distributions

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Number of draws to remove from the beginning of the Monte Carlo sample to reduce transient effects, specified as the comma-separated pair consisting of 'BurnIn' and a nonnegative scalar. For details on how estimate reduces the full Monte Carlo sample, see Algorithms.

Tip

To help you specify the appropriate burn-in period size, determine the extent of the transient behavior in the Monte Carlo sample by specifying 'BurnIn',0, simulating a few thousand observations using simulate, and then plotting the paths.

Example: 'BurnIn',0

Data Types: double

Monte Carlo adjusted sample size multiplier, specified as the comma-separated pair consisting of 'Thin' and a positive integer.

The actual Monte Carlo sample size is BurnIn + NumDraws*Thin. After discarding the burn-in, estimate discards every Thin1 draws, and then retains the next. For details on how estimate reduces the full Monte Carlo sample, see Algorithms.

Tip

To reduce potential large serial correlation in the Monte Carlo sample, or to reduce the memory consumption of the draws stored in PosteriorMdl, specify a large value for Thin.

Example: 'Thin',5

Data Types: double

Starting values of the regression coefficients for the Markov chain Monte Carlo (MCMC) sample, specified as the comma-separated pair consisting of 'BetaStart' and a numeric column vector with (PriorMdl.Intercept + PriorMdl.NumPredictors) elements. By default, BetaStart is the ordinary least-squares (OLS) estimate.

Tip

A good practice is to run estimate multiple times using different parameter starting values. Verify that the solutions from each run converge to similar values.

Example: 'BetaStart',[1; 2; 3]

Data Types: double

Starting values of the disturbance variance for the MCMC sample, specified as the comma-separated pair consisting of 'Sigma2Start' and a positive numeric scalar. By default, Sigma2Start is the OLS residual mean squared error.

Tip

A good practice is to run estimate multiple times using different parameter starting values. Verify that the solutions from each run converge to similar values.

Example: 'Sigma2Start',4

Data Types: double

Options for Custom Prior Distributions

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Reparameterization of σ2 as log(σ2) during posterior estimation and simulation, specified as the comma-separated pair consisting of 'Reparameterize' and a value in this table.

ValueDescription
falseestimate does not reparameterize σ2.
trueestimate reparameterizes σ2 as log(σ2). estimate converts results back to the original scale and does not change the functional form of PriorMdl.LogPDF.

Tip

If you experience numeric instabilities during the posterior estimation or simulation of σ2, then specify 'Reparameterize',true.

Example: 'Reparameterize',true

Data Types: logical

MCMC sampler, specified as the comma-separated pair consisting of 'Sampler' and a value in this table.

ValueDescription
'slice'Slice sampler
'metropolis'Random walk Metropolis sampler
'hmc'Hamiltonian Monte Carlo (HMC) sampler

Tip

• To increase the quality of the MCMC draws, tune the sampler.

1. Before calling estimate, specify the tuning parameters and their values by using sampleroptions. For example, to specify the slice sampler width width, use:

options = sampleroptions('Sampler',"slice",'Width',width);

2. Specify the object containing the tuning parameter specifications returned by sampleroptions by using the 'Options' name-value pair argument. For example, to use the tuning parameter specifications in options, specify:

'Options',options

• If you specify the HMC sampler, then a best practice is to provide the gradient for some variables, at least. estimate resorts the numerical computation of any missing partial derivatives (NaN values) in the gradient vector.

Example: 'Sampler',"hmc"

Data Types: string

Sampler options, specified as the comma-separated pair consisting of 'Options' and a structure array returned by sampleroptions. Use 'Options' to specify the MCMC sampler and its tuning-parameter values.

Example: 'Options',sampleroptions('Sampler',"hmc")

Data Types: struct

Typical sampling-interval width around the current value in the marginal distributions for the slice sampler, specified as the comma-separated pair consisting of 'Width' and a positive numeric scalar or a (PriorMdl.Intercept + PriorMdl.NumPredictors + 1)-by-1 numeric vector of positive values. The first element corresponds to the model intercept, if one exists in the model. The next PriorMdl.NumPredictors elements correspond to the coefficients of the predictor variables ordered by the predictor data columns. The last element corresponds to the model variance.

• If Width is a scalar, then estimate applies Width to all PriorMdl.NumPredictors + PriorMdl.Intercept + 1 marginal distributions.

• If Width is a numeric vector, then estimate applies the first element to the intercept (if one exists), the next PriorMdl.NumPredictors elements to the regression coefficients corresponding to the predictor variables in X, and the last element to the disturbance variance.

• If the sample size (size(X,1)) is less than 100, then Width is 10 by default.

• If the sample size is at least 100, then estimate sets Width to the vector of corresponding posterior standard deviations by default, assuming a diffuse prior model (diffuseblm).

The typical width of the slice sampler does not affect convergence of the MCMC sample. It does affect the number of required function evaluations, that is, the efficiency of the algorithm. If the width is too small, then the algorithm can implement an excessive number of function evaluations to determine the appropriate sampling width. If the width is too large, then the algorithm might have to decrease the width to an appropriate size, which requires function evaluations.

estimate sends Width to the slicesample function. For more details, see slicesample.

Tip

• For maximum flexibility, specify the slice sampler width width by using the 'Options' name-value pair argument. For example:

'Options',sampleroptions('Sampler',"slice",'Width',width)

Example: 'Width',[100*ones(3,1);10]

## Output Arguments

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Bayesian linear regression model storing distribution characteristics, returned as a conjugateblm, semiconjugateblm, diffuseblm, empiricalblm, or customblm model object.

• If you do not specify either Beta or Sigma2 (their values are []), then estimate updates the prior model using the data likelihood to form the posterior distribution. PosteriorMdl characterizes the posterior distribution. Its object type depends on the prior model type (PriorMdl).

Model ObjectPriorMdl
conjugateblm conjugateblm or diffuseblm
empiricalblm semiconjugateblm, empiricalblm, or customblm

• If you specify either Beta or Sigma2, then PosteriorMdl equals PriorMdl (the two models are the same object storing the same property values). estimate does not update the prior model to form the posterior model. However, estBeta, EstBetaCov, estSigma2, estSigma2Var, and Summary store conditional posterior estimates.

For more details on the display of PosteriorMdl, see Summary.

For details on supported posterior distributions that are analytically tractable, see Analytically Tractable Posteriors.

Summary of Bayesian estimators, returned as a table. Summary contains the same information as the display of the estimation summary (Display). Rows correspond to parameters, and columns correspond to these posterior characteristics for each parameter:

• Mean – Posterior mean

• Std – Posterior standard deviation

• CI95 – 95% equitailed credible interval

• Positive – The posterior probability the parameter is greater than 0

• Distribution – Description of the marginal or conditional posterior distribution of the parameter, when known

• Covariances – Estimated covariance matrix of the coefficients and disturbance variance

Row names are the names in PriorMdl.VarNames. The name of the last row is Sigma2.

Alternatively, pass PosteriorMdl to summarize to obtain a summary of Bayesian estimators.

## Limitations

If PriorMdl is an empiricalblm model object. You cannot specify Beta or Sigma2. You cannot estimate conditional posterior distributions by using an empirical prior distribution.

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### Bayesian Linear Regression Model

A Bayesian linear regression model treats the parameters β and σ2 in the multiple linear regression (MLR) model yt = xtβ + εt as random variables.

For times t = 1,...,T:

• yt is the observed response.

• xt is a 1-by-(p + 1) row vector of observed values of p predictors. To accommodate a model intercept, x1t = 1 for all t.

• β is a (p + 1)-by-1 column vector of regression coefficients corresponding to the variables that compose the columns of xt.

• εt is the random disturbance with a mean of zero and Cov(ε) = σ2IT×T, while ε is a T-by-1 vector containing all disturbances. These assumptions imply that the data likelihood is

$\ell \left(\beta ,{\sigma }^{2}|y,x\right)=\prod _{t=1}^{T}\varphi \left({y}_{t};{x}_{t}\beta ,{\sigma }^{2}\right).$

ϕ(yt;xtβ,σ2) is the Gaussian probability density with mean xtβ and variance σ2 evaluated at yt;.

Before considering the data, you impose a joint prior distribution assumption on (β,σ2). In a Bayesian analysis, you update the distribution of the parameters by using information about the parameters obtained from the likelihood of the data. The result is the joint posterior distribution of (β,σ2) or the conditional posterior distributions of the parameters.

## Tips

• Monte Carlo simulation is subject to variation. If estimate uses Monte Carlo simulation, then estimates and inferences might vary when you call estimate multiple times under seemingly equivalent conditions. To reproduce estimation results, set a random number seed by using rng before calling estimate.

• If estimate issues an error while estimating the posterior distribution using a custom prior model, then try adjusting initial parameter values by using BetaStart or Sigma2Start, or try adjusting the declared log prior function, and then reconstructing the model. The error might indicate that the log of the prior distribution is –Inf at the specified initial values.

## Algorithms

• Whenever the prior distribution (PriorMdl) and the data likelihood yield an analytically tractable posterior distribution, estimate evaluates the closed-form solutions to Bayes estimators. Otherwise, estimate resorts to Monte Carlo simulation to estimate parameters and draw inferences. For more details, see Posterior Estimation and Inference.

• This figure illustrates how estimate reduces the Monte Carlo sample using the values of NumDraws, Thin, and BurnIn. Rectangles represent successive draws from the distribution. estimate removes the white rectangles from the Monte Carlo sample. The remaining NumDraws black rectangles compose the Monte Carlo sample.

## Compatibility Considerations

expand all

Errors starting in R2019b

### Functions

Introduced in R2017a