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# Beta distribution

This section contains functions for working with beta distribution. They allow to calculate density, probability, quantiles and to generate pseudo-random numbers distributed according to the corresponding law. The beta distribution is defined by the following formula:

where:

• x – value of the random variable
• a – the first parameter of beta distribution
• b – the second parameter of beta distribution

In addition to the calculation of the individual random variables, the library also implements the ability to work with arrays of random variables.

 Function Description MathProbabilityDensityBeta Calculates the probability density function of the beta distribution MathCumulativeDistributionBeta Calculates the value of the beta probability distribution function MathQuantileBeta Calculates the value of the inverse beta distribution function for the specified probability MathRandomBeta Generates a pseudorandom variable/array of pseudorandom variables distributed according to the beta distribution law MathMomentsBeta Calculates the theoretical numerical values of the first 4 moments of the beta distribution

Example:

 #include  #include  #include  #property script_show_inputs //--- input parameters input double alpha=2;   // the first parameter of beta distribution (shape1) input double beta=5;    // the second parameter of beta distribution (shape2) //+------------------------------------------------------------------+ //| Script program start function                                    | //+------------------------------------------------------------------+ void OnStart()   { //--- hide the price chart    ChartSetInteger(0,CHART_SHOW,false); //--- initialize the random number generator    MathSrand(GetTickCount()); //--- generate a sample of the random variable    long chart=0;    string name="GraphicNormal";    int n=1000000;       // the number of values in the sample    int ncells=51;       // the number of intervals in the histogram    double x[];          // centers of the histogram intervals    double y[];          // the number of values from the sample falling within the interval    double data[];       // sample of random values    double max,min;      // the maximum and minimum values in the sample //--- obtain a sample from the beta distribution    MathRandomBeta(alpha,beta,n,data); //--- calculate the data to plot the histogram    CalculateHistogramArray(data,x,y,max,min,ncells); //--- obtain the sequence boundaries and the step for plotting the theoretical curve    double step;    GetMaxMinStepValues(max,min,step);    step=MathMin(step,(max-min)/ncells); //--- obtain the theoretically calculated data at the interval of [min,max]    double x2[];    double y2[];    MathSequence(min,max,step,x2);    MathProbabilityDensityBeta(x2,alpha,beta,false,y2); //--- set the scale    double theor_max=y2[ArrayMaximum(y2)];    double sample_max=y[ArrayMaximum(y)];    double k=sample_max/theor_max;    for(int i=0; i=cells) ind=cells-1;       frequency[ind]++;      }    return (true);   } //+------------------------------------------------------------------+ //|  Calculates values for sequence generation                       | //+------------------------------------------------------------------+ void GetMaxMinStepValues(double &maxv,double &minv,double &stepv)   { //--- calculate the absolute range of the sequence to obtain the precision of normalization    double range=MathAbs(maxv-minv);    int degree=(int)MathRound(MathLog10(range)); //--- normalize the maximum and minimum values to the specified precision    maxv=NormalizeDouble(maxv,degree);    minv=NormalizeDouble(minv,degree); //--- sequence generation step is also set based on the specified precision    stepv=NormalizeDouble(MathPow(10,-degree),degree);    if((maxv-minv)/stepv<10)       stepv/=10.;   }

Updated: 2017.02.06