matlab分布概率密度函数

1. 分布matlab怎么写

分布matlab怎么写

1.如何用matlab绘制分布图（如正太分布之类）

没有用过直接画正态分布图的函数

不过我给你说个方法

有期望直为a，方差为b，可以把正态分布的函数表达式写出来，然后用plot（或者plot3,ezplot）函数画函数的图形，这样得到的也是正态分布图形

给你举个例子：

>> x=3+randn(500,1);

>> mean(x)

ans =

2.9648

>> std(x)

ans =

1.0134

>> y=normpdf(x,3,1);

>> plot(x,y,'.')

2.正态分布的密度函数用matlab怎么写啊

MATLAB里有直接的函数。调用语法如下：（正态分布又被称为高斯分布）

y = gaussmf(x,[sig c])

其中x是变量，sig就是你图片里的σ，而c就是你图片里的μ，比如：

下面是一个例子，你可以直接复制到MATLAB中运行就可以得到一个方差为2，均值为5的正态分布函数了：

x=0:0.1:10;

y=gaussmf(x,[2 5]);

plot(x,y)

xlabel('gaussmf, P=[2 5]')

3.matlab怎么画正态分布图

x = (0:0.02:10);

y = lognpdf(x,1.73,0.22);

figure,subplot(121);

plot(x,y);

grid;

xlabel('x'); ylabel('p')

%这是对数正态分布

x = (0:0.02:10);

y = normpdf(x,1.73,0.22);

subplot(122);

plot(x,y);

grid;

xlabel('x'); ylabel('p')

%这是正态分布

4.急~~~~标准正态分布的Q函数用 matlab 怎么写

Matlab中本身有Q函数，即qfunc（) 其反函数是qfuncinv()

--------------------------------

help qfunc

qfunc

Q function

Syntax

y = qfunc(x)

Description

y = qfunc(x) is one minus the cumulative distribution function of the standardized normal random variable, evaluated at each element of the real array x. For a scalar x, the formula is

The Q function is related to the complementary error function, erfc, according to

Examples

The example below computes the Q function on a matrix, element by element.

x = [0 1 2; 3 4 5];

format short e % Switch to floating point format for displays.

y = qfunc(x)

format % Return to default format for displays.

The output is below.

y =

5.0000e-001 1.5866e-001 2.2750e-002

1.3499e-003 3.1671e-005 2.8665e-007

--------------------------------------------

help qfuncinv

qfuncinv

Inverse Q function

Syntax

y = qfuncinv(x)

Description

y = qfuncinv(x) returns the argument of the Q function at which the Q function's value is x. The input x must be a real array with elements between 0 and 1, inclusive.

For a scalar x, the Q function is one minus the cumulative distribution function of the standardized normal random variable, evaluated at x. The Q function is defined as

The Q function is related to the complementary error function, erfc, according to

Examples

The example below illustrates the inverse relationship between qfunc and qfuncinv.

x1 = [0 1 2; 3 4 5];

y1 = qfuncinv(qfunc(x1)) % Invert qfunc to recover x1.

x2 = 0:.2:1;

y2 = qfunc(qfuncinv(x2)) % Invert qfuncinv to recover x2.

The output is below.

y1 =

0 1 2

3 4 5

y2 =