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--- prob [2012/12/06 18:12] – ignacy
+++ prob [2012/12/06 19:08] – ignacy
@@ Line 2: / Line 2: @@
 Ignacy Misztal and Deukhwan Lee, University of Georgia \\
 /29/99-04/19/2001 \\
-==== Introduction ====
 Module Prob is a collection of random number generators / probabilities /
@@ Line 15: / Line 13: @@
 random number generators. Some low level code is from Luis Varona.\\
-The module contains the following subroutines/functions:
+==== Subroutines/functions====
 ''call set_seed(n)'' \\
@@ Line 21: / Line 19: @@
 called, the seed will be selected by the system. \\
 \\
 ''x=gen_uniform(a,b)''\\
 a,b - both real (r*) or both integers or both missing.\\
-If a,b are missing, generates samples from uniform(0,1) distribution. \\
+If a,b are missing, generates samples from uniform(0,1) distribution \\
-If a,b are real (r8), generates samples from uniform(a,b) distribution. \\
+If a,b are real (r8), generates samples from uniform(a,b) distribution \\
-If a,b are integers, generates random integer between a and b.
+If a,b are integers, generates random integer between a and b \\
+\\
 ''x=gen_normal(mean,var)''\\
 mean - (r8) scalar or vector\\
@@ Line 34: / Line 34: @@
 Generates x=N(mean,Var) when mean and var are scalars, or
 x=MVN(mean,Var) when mean is a vector and Var is a matrix.
 Arguments mean and var are optional. If they are missing, sampling is
-from N(0,1)
+from N(0,1)\\
+\\
+''x=gen_invwishart(inv_q_form,df)''\\
+inv_q_form - (r8) scalar or square matrix containing inverse of quadratic form\\
+df         - an integer containing degrees of freedom\\
-=== Gen_invwishart ===
+Generates samples from inverted chi square or inverted Wishart distributions.\\
+\\
+''y=normal(x)''\\
+x - real(r8) scalar\\
+y  - real (r8) contains density(X) for N(0,1)\\
+\\
+''y=normal_cdf(x)''\\
+x - real (r8) scalar\\
+y - real (r8) cumulative distribution function for N(0,1)\\
+\\
- x=gen_invwishart(inv_q_form,df)
+''y=normal_invcdf(x)''\\
+x - real (r8) scalar in the range of <0,1>\\
-inv_q_form - (r8) scalar or square matrix containing inverse of quadratic form
+y - real (r8) as in: x=normal_cdf(y)\\
+\\
-df         - an integer containing degrees of freedom
-Generates samples from inverted chi square or inverted Wishart distributions.
-=== Normal ===
-y=normal(x)
-x - real(r8) scalar
-y  - real (r8) contains density(X) for N(0,1)
-=== Normal_cdf ===
-y=normal_cdf(x)
-x - real (r8) scalar
-y - real (r8) cumulative distribution function for N(0,1)
-=== Normal_invcdf ===
-y=normal_invcdf(x)
-x - real (r8) scalar in the range of <0,1>.
-y - - real (r8) as in: x=normal_cdf(y)
-=== Gen_trunc_normal ===
-y=generate_trunc_normal(a,b,mean,var)
-y - real (r8) scalar or vector
-a,b - real (r8) lower and upper bound of random samples
-mean - real(r8) scalar or vectors of mean, optional if scalar
-var - real(r8) scalar or matricex of variances, optional if scalar
+''y=generate_trunc_normal(a,b,mean,var)''\\
+y - real (r8) scalar or vector\\
+a,b - real (r8) lower and upper bound of random samples\\
+mean - real(r8) scalar or vectors of mean, optional if scalar\\
+var - real(r8) variance or covariance matrix, optional if scalar\\
+\\
 If mean and var are missing, generates random samples from N(0,1) distribution
-truncated to interval <a,b>.
+truncated to interval <a,b>.\\
+\\
 If mean and var are scalars, generates random samples from N(mean,var)
-distribution truncated to interval <a,b>.
+distribution truncated to interval <a,b>.\\
 If mean is a vector and var is a matrix, generates random samples from
-MVN(mean,var) distribution with first dimension truncated to interval <a,b>.
+MVN(mean,var) distribution with first dimension truncated to interval <a,b>.\\
+\\
 === Other functions/subroutines ===
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