[Octave-cvsupdate] SF.net SVN: octave:[10633] trunk/octave-forge/main/queueing/inst

[<mma...@us...>]

Revision: 10633
          http://octave.svn.sourceforge.net/octave/?rev=10633&view=rev
Author:   mmarzolla
Date:     2012-06-16 15:40:34 +0000 (Sat, 16 Jun 2012)
Log Message:
-----------
Small fixed to the docstrings

Modified Paths:
--------------
    trunk/octave-forge/main/queueing/inst/ctmc.m
    trunk/octave-forge/main/queueing/inst/dtmc.m
    trunk/octave-forge/main/queueing/inst/qnsolve.m

Modified: trunk/octave-forge/main/queueing/inst/ctmc.m
===================================================================
--- trunk/octave-forge/main/queueing/inst/ctmc.m	2012-06-16 09:34:54 UTC (rev 10632)
+++ trunk/octave-forge/main/queueing/inst/ctmc.m	2012-06-16 15:40:34 UTC (rev 10633)
@@ -28,13 +28,12 @@
 ## Compute stationary or transient state occupancy probabilities
 ## for a continuous-time Markov chain.
 ##
-## With a single argument, compute the stationary state occupancy
-## probability vector @var{p}(1), @dots{}, @var{p}(N) for a
-## continuous-time Markov chain with @math{N \times N} infinitesimal
-## generator matrix @var{Q}. With three arguments, compute the state
-## occupancy probabilities @var{p}(1), @dots{}, @var{p}(N) at time
-## @var{t}, given initial state occupancy probabilities @var{p0} at time
-## 0.
+## With a single argument, compute the stationary probability vector
+## @var{p}(1), @dots{}, @var{p}(N) for a continuous-time Markov chain
+## with @math{N \times N} infinitesimal generator matrix @var{Q}. With
+## three arguments, compute the state occupancy probabilities
+## @var{p}(1), @dots{}, @var{p}(N) at time @var{t}, given initial state
+## occupancy probabilities @var{p0}(1), @dots{}, @var{p0}(N) at time 0.
 ##
 ## @strong{INPUTS}
 ##
@@ -44,10 +43,12 @@
 ## Infinitesimal generator matrix. @var{Q} is a @math{N \times N} square
 ## matrix where @code{@var{Q}(i,j)} is the transition rate from state
 ## @math{i} to state @math{j}, for @math{1 @leq{} i \neq j @leq{} N}.
-## Transition rates must be nonnegative, and @math{\sum_{j=1}^N Q_{i, j} = 0}
+## #var{Q} must satisfy the property that @math{\sum_{j=1}^N Q_{i, j} =
+## 0}
 ##
 ## @item t
-## Time at which to compute the transient probability
+## Time at which to compute the transient probability. If omitted,
+## compute the steady state occupancy probability.
 ##
 ## @item p0
 ## @code{@var{p0}(i)} is the probability that the system
@@ -66,7 +67,7 @@
 ## satisfies the equation @math{p{\bf Q} = 0} and @math{\sum_{i=1}^N p_i = 1}.
 ## If this function is invoked with three arguments, @code{@var{p}(i)}
 ## is the probability that the system is in state @math{i} at time @var{t},
-## given the initial occupancy probabilities @var{p0}.
+## given the initial occupancy probabilities @var{p0}(1), @dots{}, @var{p0}(N).
 ##
 ## @end table
 ##

Modified: trunk/octave-forge/main/queueing/inst/dtmc.m
===================================================================
--- trunk/octave-forge/main/queueing/inst/dtmc.m	2012-06-16 09:34:54 UTC (rev 10632)
+++ trunk/octave-forge/main/queueing/inst/dtmc.m	2012-06-16 15:40:34 UTC (rev 10633)
@@ -27,13 +27,14 @@
 ## @cindex Markov chain, transient probabilities
 ## @cindex Transient probabilities
 ##
-## Compute steady-state or transient state occupancy probabilities for a
-## Discrete-Time Markov Chain. With a single argument, compute the
-## steady-state occupancy probability vector @code{@var{p}(1), @dots{},
-## @var{p}(N)} given the @math{N \times N} transition probability matrix
-## @var{P}. With three arguments, compute the state occupancy
-## probabilities @code{@var{p}(1), @dots{}, @var{p}(N)} after @var{n}
-## steps, given initial occupancy probability vector @var{p0}.
+## With a single argument, compute the steady-state occupancy
+## probability vector @code{@var{p}(1), @dots{}, @var{p}(N)} for a
+## discrete-time Markov chain described by the @math{N \times N}
+## transition probability matrix @var{P}. With three arguments, compute
+## the state occupancy probabilities @code{@var{p}(1), @dots{},
+## @var{p}(N)} that the system is in state @math{i} after @var{n} steps,
+## given initial occupancy probability vector @var{p0}(1), @dots{},
+## @var{p0}(N).
 ##
 ## @strong{INPUTS}
 ##
@@ -42,8 +43,9 @@
 ## @item P
 ## @code{@var{P}(i,j)} is the transition probability from state @math{i}
 ## to state @math{j}. @var{P} must be an irreducible stochastic matrix,
-## which means that the sum of each row must be 1 (@math{\sum_{j=1}^N P_{i, j} = 1}), and the rank of
-## @var{P} must be equal to its dimension.
+## which means that the sum of each row must be 1 (@math{\sum_{j=1}^N
+## P_{i, j} = 1}), and the rank of @var{P} must be equal to its
+## dimension.
 ##
 ## @item n
 ## Number of transitions after which compute the state occupancy probabilities

Modified: trunk/octave-forge/main/queueing/inst/qnsolve.m
===================================================================
--- trunk/octave-forge/main/queueing/inst/qnsolve.m	2012-06-16 09:34:54 UTC (rev 10632)
+++ trunk/octave-forge/main/queueing/inst/qnsolve.m	2012-06-16 15:40:34 UTC (rev 10633)
@@ -22,8 +22,7 @@
 ## @deftypefnx {Function File} {[@var{U}, @var{R}, @var{Q}, @var{X}] =} qnsolve (@var{"open"}, @var{lambda}, @var{QQ}, @var{V})
 ## @deftypefnx {Function File} {[@var{U}, @var{R}, @var{Q}, @var{X}] =} qnsolve (@var{"mixed"}, @var{lambda}, @var{N}, @var{QQ}, @var{V})
 ##
-## General evaluator of QN models. Networks can be open,
-## closed or mixed; single as well as multiclass networks are supported.
+## High-level function for analyzing QN models.
 ##
 ## @itemize
 ##

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