diff --git a/src/app/credExplorer/basicPagerank.js b/src/app/credExplorer/basicPagerank.js
index 519553e..65249b3 100644
--- a/src/app/credExplorer/basicPagerank.js
+++ b/src/app/credExplorer/basicPagerank.js
@@ -9,6 +9,7 @@ import type {
   Distribution,
   SparseMarkovChain,
 } from "../../core/attribution/markovChain";
+import {uniformDistribution} from "../../core/attribution/markovChain";
 
 export type PagerankResult = AddressMap<{|
   +address: Address,
@@ -132,10 +133,6 @@ function sparseMarkovChainAction(
   return result;
 }
 
-function uniformDistribution(n: number): Distribution {
-  return new Float64Array(n).fill(1 / n);
-}
-
 function findStationaryDistribution(chain: SparseMarkovChain): Distribution {
   let r0 = uniformDistribution(chain.length);
   function computeDelta(pi0, pi1) {
diff --git a/src/core/attribution/markovChain.js b/src/core/attribution/markovChain.js
index d36dbfd..3ea3723 100644
--- a/src/core/attribution/markovChain.js
+++ b/src/core/attribution/markovChain.js
@@ -65,3 +65,10 @@ export function sparseMarkovChainFromTransitionMatrix(
     };
   });
 }
+
+export function uniformDistribution(n: number): Distribution {
+  if (isNaN(n) || !isFinite(n) || n !== Math.floor(n) || n <= 0) {
+    throw new Error("expected positive integer, but got: " + n);
+  }
+  return new Float64Array(n).fill(1 / n);
+}
diff --git a/src/core/attribution/markovChain.test.js b/src/core/attribution/markovChain.test.js
index b231f78..0b07197 100644
--- a/src/core/attribution/markovChain.test.js
+++ b/src/core/attribution/markovChain.test.js
@@ -1,6 +1,9 @@
 // @flow
 
-import {sparseMarkovChainFromTransitionMatrix} from "./markovChain";
+import {
+  sparseMarkovChainFromTransitionMatrix,
+  uniformDistribution,
+} from "./markovChain";
 
 describe("sparseMarkovChainFromTransitionMatrix", () => {
   it("works for a simple matrix", () => {
@@ -77,3 +80,19 @@ describe("sparseMarkovChainFromTransitionMatrix", () => {
     );
   });
 });
+
+describe("uniformDistribution", () => {
+  it("computes the uniform distribution with domain of size 1", () => {
+    const pi = uniformDistribution(1);
+    expect(pi).toEqual(new Float64Array([1]));
+  });
+  it("computes the uniform distribution with domain of size 4", () => {
+    const pi = uniformDistribution(4);
+    expect(pi).toEqual(new Float64Array([0.25, 0.25, 0.25, 0.25]));
+  });
+  [0, -1, Infinity, NaN, 3.5, '"beluga"', null, undefined].forEach((bad) => {
+    it(`fails when given domain ${String(bad)}`, () => {
+      expect(() => uniformDistribution((bad: any))).toThrow("positive integer");
+    });
+  });
+});