给出SBL算法的matlab代码

下面是SBL算法的Matlab代码示例：

function [w, alpha, beta, gamma] = SBL(X, y, lambda, tol)
% X: input data matrix, y: output vector
% lambda: regularization parameter, tol: convergence tolerance
[N, D] = size(X);
w = zeros(D, 1);
alpha = ones(D, 1) * 1e-6;
beta = 1 / var(y);
gamma = ones(D, 1);
converged = false;
while ~converged
    % E-step
    Sigma = diag(alpha) + beta * (X' * X);
    Sigma_inv = inv(Sigma);
    m = beta * Sigma_inv * X' * y;
    gamma = 1 - alpha .* diag(Sigma_inv);
    % M-step
    alpha_prev = alpha;
    for i = 1:D
        alpha(i) = gamma(i) / (m(i)^2 + lambda);
    end
    beta = (N - sum(gamma)) / norm(y - X * m)^2;
    % check convergence
    if max(abs(alpha - alpha_prev)) < tol
        converged = true;
    end
end
w = m;
end

其中，`X`是输入数据矩阵，`y`是输出向量，`lambda`是正则化参数，`tol`是收敛容差。函数返回的是权重向量`w`、精度参数`alpha`、噪声精度参数`beta`和关键性参数`gamma`。该算法使用了EM算法来估计模型参数，其中E-step计算后验精度和权重均值，M-step更新精度参数和噪声精度参数。通过比较不同迭代之间的精度参数变化来判断算法是否收敛。