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diff --git a/modules/probabilities/src/examples/clojure/examples/finite_distributions.clj b/modules/probabilities/src/examples/clojure/examples/finite_distributions.clj
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+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;
+;; Probability distribution application examples
+;;
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+
+(ns
+  #^{:author "Konrad Hinsen"
+     :skip-wiki true
+     :doc "Examples for finite probability distribution"}
+  examples.finite-distributions
+  (:use [clojure.contrib.probabilities.finite-distributions
+	 :only (uniform prob cond-prob join-with dist-m choose
+	        normalize certainly cond-dist-m normalize-cond)])
+  (:use [clojure.contrib.monads
+	 :only (domonad with-monad m-seq m-chain m-lift)])
+  (:require clojure.contrib.accumulators))
+
+;; Simple examples using dice
+
+; A single die is represented by a uniform distribution over the
+; six possible outcomes.
+(def die (uniform #{1 2 3 4 5 6}))
+
+; The probability that the result is odd...
+(prob odd? die)
+; ... or greater than four.
+(prob #(> % 4) die)
+
+; The sum of two dice
+(def two-dice (join-with + die die))
+(prob #(> % 6) two-dice)
+
+; The sum of two dice using a monad comprehension
+(assert (= two-dice
+	   (domonad dist-m
+		    [d1 die
+		     d2 die]
+		    (+ d1 d2))))
+
+; The two values separately, but as an ordered pair
+(domonad dist-m
+  [d1 die
+   d2 die]
+  (if (< d1 d2) (list d1 d2) (list d2 d1)))
+
+; The conditional probability for two dice yielding X if X is odd:
+(cond-prob odd? two-dice)
+
+; A two-step experiment: throw a die, and then add 1 with probability 1/2
+(domonad dist-m
+  [d die
+   x (choose (/ 1 2)  d
+	     :else    (inc d))]
+  x)
+
+; The sum of n dice
+(defn dice [n]
+   (domonad dist-m
+      [ds (m-seq (replicate n die))]
+      (apply + ds)))
+
+(assert (= two-dice (dice 2)))
+
+(dice 3)
+
+
+;; Construct an empirical distribution from counters
+
+; Using an ordinary counter:
+(def dist1
+  (normalize
+    (clojure.contrib.accumulators/add-items
+      clojure.contrib.accumulators/empty-counter
+      (for [_ (range 1000)] (rand-int 5)))))
+
+; Or, more efficiently, using a counter that already keeps track of its total:
+(def dist2
+  (normalize
+    (clojure.contrib.accumulators/add-items
+      clojure.contrib.accumulators/empty-counter-with-total
+      (for [_ (range 1000)] (rand-int 5)))))
+
+
+;; The Monty Hall game
+;; (see http://en.wikipedia.org/wiki/Monty_Hall_problem for a description)
+
+; The set of doors. In the classical variant, there are three doors,
+; but the code can also work with more than three doors.
+(def doors #{:A :B :C})
+
+; A simulation of the game, step by step:
+(domonad dist-m
+  [; The prize is hidden behind one of the doors.
+   prize  (uniform doors)
+   ; The player make his initial choice.
+   choice (uniform doors)
+   ; The host opens a door which is neither the prize door nor the
+   ; one chosen by the player.
+   opened (uniform (disj doors prize choice))
+   ; If the player stays with his initial choice, the game ends and the
+   ; following line should be commented out. It describes the switch from
+   ; the initial choice to a door that is neither the opened one nor
+   ; his original choice.
+   choice (uniform (disj doors opened choice))
+   ]
+  ; If the chosen door has the prize behind it, the player wins.
+  (if (= choice prize) :win :loose))
+
+
+;; Tree growth simulation
+;; Adapted from the code in:
+;; Martin Erwig and Steve Kollmansberger,
+;; "Probabilistic Functional Programming in Haskell",
+;; Journal of Functional Programming, Vol. 16, No. 1, 21-34, 2006
+;; http://web.engr.oregonstate.edu/~erwig/papers/abstracts.html#JFP06a
+
+; A tree is represented by two attributes: its state (alive, hit, fallen),
+; and its height (an integer). A new tree starts out alive and with zero height.
+(def new-tree {:state :alive, :height 0})
+
+; An evolution step in the simulation modifies alive trees only. They can
+; either grow by one (90% probability), be hit by lightning and then stop
+; growing (4% probability), or fall down (6% probability).
+(defn evolve-1 [tree]
+  (let [{s :state h :height} tree]
+    (if (= s :alive)
+      (choose 0.9   (assoc tree :height (inc (:height tree)))
+	      0.04  (assoc tree :state :hit) 
+	      :else {:state :fallen, :height 0})
+      (certainly tree))))
+
+; Multiple evolution steps can be chained together with m-chain,
+; since each step's input is the output of the previous step.
+(with-monad dist-m
+  (defn evolve [n tree]
+    ((m-chain (replicate n evolve-1)) tree)))
+
+; Try it for zero, one, or two steps.
+(evolve 0 new-tree)
+(evolve 1 new-tree)
+(evolve 2 new-tree)
+
+; We can also get a distribution of the height only:
+(with-monad dist-m
+  ((m-lift 1 :height) (evolve 2 new-tree)))
+
+
+
+;; Bayesian inference
+;;
+;; Suppose someone has three dice, one with six faces, one with eight, and
+;; one with twelve. This person throws one die and gives us the number,
+;; but doesn't tell us which die it was. What are the Bayesian probabilities
+;; for each of the three dice, given the observation we have?
+
+; A function that returns the distribution of a dice with n faces.
+(defn die-n [n] (uniform (range 1 (inc n))))
+
+; The three dice in the game with their distributions. With this map, we
+; can easily calculate the probability for an observation under the
+; condition that a particular die was used.
+(def dice {:six     (die-n 6)
+	   :eight   (die-n 8)
+	   :twelve  (die-n 12)})
+
+; The only prior knowledge is that one of the three dice is used, so we
+; have no better than a uniform distribution to start with.
+(def prior (uniform (keys dice)))
+
+; Add a single observation to the information contained in the
+; distribution. Adding an observation consists of
+; 1) Draw a die from the prior distribution.
+; 2) Draw an observation from the distribution of that die.
+; 3) Eliminate (replace by nil) the trials that do not match the observation.
+; 4) Normalize the distribution for the non-nil values.
+(defn add-observation [prior observation]
+  (normalize-cond
+    (domonad cond-dist-m
+      [die    prior
+       number (get dice die)
+       :when  (= number observation) ]
+      die)))
+
+; Add one observation.
+(add-observation prior 1)
+
+; Add three consecutive observations.
+(-> prior (add-observation 1)
+          (add-observation 3)
+	  (add-observation 7))
+
+; We can also add multiple observations in a single trial, but this
+; is slower because more combinations have to be taken into account.
+; With Bayesian inference, it is most efficient to eliminate choices
+; as early as possible.
+(defn add-observations [prior observations]
+  (with-monad cond-dist-m
+    (let [n-nums #(m-seq (replicate (count observations) (get dice %)))]
+      (normalize-cond
+        (domonad
+          [die    prior
+           nums   (n-nums die)
+	   :when  (= nums observations)]
+	  die)))))
+
+(add-observations prior [1 3 7])
diff --git a/modules/probabilities/src/examples/clojure/examples/monte_carlo.clj b/modules/probabilities/src/examples/clojure/examples/monte_carlo.clj
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index 00000000..4583dcf9
--- /dev/null
+++ b/modules/probabilities/src/examples/clojure/examples/monte_carlo.clj
@@ -0,0 +1,73 @@
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;
+;; Monte-Carlo application examples
+;;
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
+
+(ns
+  #^{:author "Konrad Hinsen"
+     :skip-wiki true
+     :doc "Examples for monte carlo methods"}
+  examples.monte-carlo
+  (:require [clojure.contrib.generic.collection :as gc])
+  (:use [clojure.contrib.probabilities.random-numbers
+	 :only (lcg rand-stream)])
+  (:use [clojure.contrib.probabilities.finite-distributions
+	 :only (uniform)])
+  (:use [clojure.contrib.probabilities.monte-carlo
+	 :only (random-stream discrete interval normal lognormal exponential
+		n-sphere
+		sample sample-sum sample-mean sample-mean-variance)]
+	:reload)
+  (:use [clojure.contrib.monads
+	:only (domonad state-m)]))
+
+; Create a linear congruential generator
+(def urng (lcg 259200 7141 54773 1))
+
+;; Use Clojure's built-in random number generator
+;(def urng rand-stream)
+
+; Sample transformed distributions
+(defn sample-distribution
+  [n rt]
+  (take n (gc/seq (random-stream rt urng))))
+
+; Interval [-2, 2)
+(sample-distribution 10 (interval -2 2))
+; Compare with a direct transformation
+(= (sample-distribution 10 (interval -2 2))
+   (map (fn [x] (- (* 4 x) 2)) (take 10 (gc/seq urng))))
+
+; Normal distribution
+(sample-distribution 10 (normal 0 1))
+
+; Log-Normal distribution
+(sample-distribution 10 (lognormal 0 1))
+
+; Exponential distribution
+(sample-distribution 10 (exponential 1))
+
+; n-sphere distribution
+(sample-distribution 10 (n-sphere 2 1))
+
+; Discrete distribution
+(sample-distribution 10 (discrete (uniform (range 1 7))))
+
+; Compose distributions in the state monad
+(def sum-two-dists
+  (domonad state-m
+    [r1 (interval -2 2)
+     r2 (normal 0 1)]
+    (+ r1 r2)))
+
+(sample-distribution 10 sum-two-dists)
+
+; Distribution transformations
+(sample-distribution  5 (sample 2 (interval -2 2)))
+(sample-distribution 10 (sample-sum 10 (interval -2 2)))
+(sample-distribution 10 (sample-mean 10 (interval -2 2)))
+(sample-distribution 10 (sample-mean-variance 10 (interval -2 2)))
+