(rebase): documented ratio of lognormals

packages/squiggle-lang/__tests__/Distributions/AlgebraicCombination_test.res

@@ -31,7 +31,7 @@ let algebraicSubtract = algebraicSubtract(~env)
 let algebraicLogarithm = algebraicLogarithm(~env)
 let algebraicPower = algebraicPower(~env)
 
-describe("Addition of distributions", () => {
+describe("(Algebraic) addition of distributions", () => {
 
     describe("mean", () => {
         test("normal(mean=5) + normal(mean=20)", () => {
@@ -44,6 +44,39 @@ describe("Addition of distributions", () => {
             -> expect
             -> toBe(Some(2.5e1))
         })
+
+        test("uniform(low=9, high=10) + beta(alpha=2, beta=5)", () => {
+            // let uniformMean = (9.0 +. 10.0) /. 2.0
+            // let betaMean = 1.0 /. (1.0 +. 5.0 /. 2.0)
+            let received = uniformDist 
+                -> algebraicAdd(betaDist)
+                -> E.R2.fmap(GenericDist_Types.Constructors.UsingDists.mean)
+                -> E.R2.fmap(run)
+                -> E.R2.fmap(toFloat)
+                -> E.R.toExn
+            switch received {
+                | None => false -> expect -> toBe(true)
+                // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad. 
+                // sometimes it works with ~digits=2. 
+                | Some(x) => x -> expect -> toBeSoCloseTo(0.1884273175239643, ~digits=1)  // (uniformMean +. betaMean)
+            }
+        })
+        test("beta(alpha=2, beta=5) + uniform(low=9, high=10)", () => {
+            // let uniformMean = (9.0 +. 10.0) /. 2.0
+            // let betaMean = 1.0 /. (1.0 +. 5.0 /. 2.0)
+            let received = betaDist 
+                -> algebraicAdd(uniformDist)
+                -> E.R2.fmap(GenericDist_Types.Constructors.UsingDists.mean)
+                -> E.R2.fmap(run)
+                -> E.R2.fmap(toFloat)
+                -> E.R.toExn
+            switch received {
+                | None => false -> expect -> toBe(true)
+                // This is nondeterministic, we could be in a situation where ci fails but you click rerun and it passes, which is bad. 
+                // sometimes it works with ~digits=2. 
+                | Some(x) => x -> expect -> toBeSoCloseTo(0.19708465087256619, ~digits=1)  // (uniformMean +. betaMean)
+            }
+        })
     })
     describe("pdf", () => {
         testAll("(normal(mean=5) + normal(mean=5)).pdf (imprecise)", list{8e0, 1e1, 1.2e1, 1.4e1}, x => {
@@ -192,7 +225,5 @@ describe("Addition of distributions", () => {
                 }
             }
         })
-
-
     })
 })

packages/squiggle-lang/__tests__/TestHelpers.res

@@ -11,8 +11,8 @@ let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Ou
 let fnImage = (theFn, inps) => Js.Array.map(theFn, inps)
 
 let env: DistributionOperation.env = {
-  sampleCount: 100,
-  xyPointLength: 100,
+  sampleCount: 100000,
+  xyPointLength: 1000,
 }
 
 let run = DistributionOperation.run(~env)

packages/squiggle-lang/src/rescript/Distributions/SymbolicDist/SymbolicDist.res

@@ -141,6 +141,8 @@ module Lognormal = {
   }
   let divide = (l1, l2) => {
     let mu = l1.mu -. l2.mu
+    // We believe the ratiands will have covariance zero. 
+    // See here https://stats.stackexchange.com/questions/21735/what-are-the-mean-and-variance-of-the-ratio-of-two-lognormal-variables for details
     let sigma = l1.sigma +. l2.sigma
     #Lognormal({mu: mu, sigma: sigma})
   }