Bayesian-maximum-a-posteriori

matlab编写的流体力学计算和传热计算程序，一些例子，主要用于流体力学计算和传热的计算，为设计实例(Matlab prepared by the computational fluid dynamics and heat transfer calculation procedures, some examples, mainly used for computational fluid dynamics and heat transfer calculation for the design example)

一个自己弄的四旋翼的非线性动力学模型的m文件(nonliner model for quadrotor)

C-means in 2 types. C=2

以前用VB6编写的一个计算器，支持常用的数学函数，支持弧度、角度转换，支持简单的函数作图。(A calculator writen by VB6, supports common mathematical functions, support radian, angle conversion, support simple function mapping.)

ansys 求解桥梁结构弯矩响线的命令流，非常实用(ansys solving the stream of the bridge structure bending ring line command is very useful)

Fitting curve distribution function拟合曲线分布函数(Fitting curve distribution function)

vc++实现的矩阵运算方面得代码，很不错的，内容绝对好吃(matrix operations, code vc++ achieve very good, the content is absolutely delicious)

％ SSOR预处理的共轭梯度法求解方程Ax=b ％ 输入参数说明 ％ A 正定矩阵[n*n] ％ b 右边向量 ％ omega SSOR预处理参数(0--2) ％ Times 迭代次数 ％ errtol 给定误差终止条件 ％ ％输出参数 ％ NewX 方程Ax=b的x近似解 ％ avgerr 求解的当前平均绝对误差( SSOR preconditioning conjugate gradient method for solving equations Ax = b the input parameter descriptions A positive definite matrix [n* n] b right side vector omega SSOR preconditioning parameters (0- 2) Times iterations errtol termination of a given error condition NewX output parameter equation Ax = b of x approximate solution to solve the current avgerr average absolute error)

说明：  经验正交函数分析FORTRAN程序，用于变量场的时空结构分离（配套书籍《现代气候统计诊断与预测技术》）。(empirical orthogonal function, FORTRAN procedure, used to separate the spatio-temporal structure of climate variables.)

％ 奇异值分解 (sigular value decomposition,SVD) 是另一种正交矩阵分解法；SVD是最可靠的分解法， ％ 但是它比QR 分解法要花上近十倍的计算时间。[U,S,V]=svd(A)，其中U和V代表二个相互正交矩阵， ％ 而S代表一对角矩阵。 和QR分解法相同者， 原矩阵A不必为正方矩阵。 ％ 使用SVD分解法的用途是解最小平方误差法和数据压缩。用svd分解法解线性方程组，在Quke2中就用这个来计算图形信息，性能相当的好。在计算线性方程组时，一些不能分解的矩阵或者严重病态矩阵的线性方程都能很好的得到解( Singular value decomposition (sigular value decomposition, SVD) is another orthogonal matrix decomposition method SVD decomposition is the most reliable method, but it takes more than QR decomposition near ten times the computing time. [U, S, V] = svd (A), in which U and V on behalf of two mutually orthogonal matrix, and the S on behalf of a diagonal matrix. And QR decomposition are the same, the original matrix A is no need for the square matrix. The use of SVD decomposition method are used as a solution of least squares error method and data compression. Using SVD decomposition solution of linear equations, in Quke2 on to use this information to calculate the graphics performance quite good. In the calculation of linear equations, some indecomposable matrix or serious pathological matrix of linear equations can be a very good solution)