add probs to lambda

src/univariate/poisson.jl

@@ -9,21 +9,6 @@ end
 
 @_jl_dist_1p Poisson pois
 
-function entropy(d::Poisson)
-    λ = d.lambda
-    if λ < 50.0
-        s = 0.0
-        for k in 1:100
-            s += λ^k * lgamma(k + 1.0) / gamma(k + 1.0)
-        end
-        return λ * (1.0 - log(λ)) + exp(-λ) * s
-    else
-        return 0.5 * log(2 * pi * e * λ) -
-               (1 / (12 * λ)) -
-               (1 / (24 * λ * λ)) -
-               (19 / (360 * λ * λ * λ))
-    end
-end
 
 isupperbounded(::Union(Poisson, Type{Poisson})) = false
 islowerbounded(::Union(Poisson, Type{Poisson})) = true
@@ -35,12 +20,52 @@ maximum(::Union(Poisson, Type{Poisson})) = Inf
 insupport(::Poisson, x::Real) = isinteger(x) && zero(x) <= x
 insupport(::Type{Poisson}, x::Real) = isinteger(x) && zero(x) <= x
 
-kurtosis(d::Poisson) = 1.0 / d.lambda
+
+function probs(d::Poisson, rgn::UnitRange)
+    λ = d.lambda
+    f, l = rgn[1], rgn[end]
+    0 <= f <= l || throw(BoundsError())
+    r = Array(Float64, l - f + 1)
+    v = r[1] = pdf(d, f)
+    if l > f
+        b = f - 1
+        for x = f+1:l
+            c = λ / x
+            r[x - b] = (v *= c)
+        end
+    end
+    return r
+end
 
 mean(d::Poisson) = d.lambda
 
 median(d::Poisson) = quantile(d, 0.5)
 
+mode(d::Poisson) = ifloor(d.lambda)
+modes(d::Poisson) = isinteger(d.lambda) ? [int(d.lambda)-1,int(d.lambda)] : [ifloor(d.lambda)]
+
+var(d::Poisson) = d.lambda
+
+skewness(d::Poisson) = 1.0 / sqrt(d.lambda)
+
+kurtosis(d::Poisson) = 1.0 / d.lambda
+
+function entropy(d::Poisson)
+    λ = d.lambda
+    if λ < 50.0
+        s = 0.0
+        for k in 1:100
+            s += λ^k * lgamma(k + 1.0) / gamma(k + 1.0)
+        end
+        return λ * (1.0 - log(λ)) + exp(-λ) * s
+    else
+        return 0.5 * log(2 * pi * e * λ) -
+               (1 / (12 * λ)) -
+               (1 / (24 * λ * λ)) -
+               (19 / (360 * λ * λ * λ))
+    end
+end
+
 function mgf(d::Poisson, t::Real)
     l = d.lambda
     return exp(l * (exp(t) - 1.0))
@@ -51,13 +76,6 @@ function cf(d::Poisson, t::Real)
     return exp(l * (exp(im * t) - 1.0))
 end
 
-mode(d::Poisson) = ifloor(d.lambda)
-modes(d::Poisson) = isinteger(d.lambda) ? [int(d.lambda)-1,int(d.lambda)] : [ifloor(d.lambda)]
-
-skewness(d::Poisson) = 1.0 / sqrt(d.lambda)
-
-var(d::Poisson) = d.lambda
-
 # model fitting
 
 immutable PoissonStats <: SufficientStats