{"action":"created","comment":{"url":"https://api.github.com/repos/gonum/stat/pulls/comments/44370603","id":44370603,"diff_hunk":"@@ -118,3 +118,99 @@ func (n *Normal) Rand(x []float64) []float64 {\n \tfloats.Add(x, n.mu)\n \treturn x\n }\n+\n+// ConditionNormal returns the Normal distribution that is the receiver conditioned\n+// on the input evidence. The returned multivariate normal has dimension\n+// n - len(dims), where n is the dimension of the original receiver. The updated\n+// mean and covariance are\n+//  mu = mu_1 + sigma_{2,1}^T * sigma_{2,2}^-1 (v - mu_2)\n+//  sigma = sigma_{1,1} - sigma_{2,1}^T * sigma_{2,2}^-1 * sigma_{2,1}\n+// where the elements of x_2 have been observed with values v. The dimension order\n+// is preserved during conditioning, so if the value of dimension 1 is observed,\n+// the returned normal represents dimensions {0, 2, ...} of the original Normal\n+// distribution.\n+//\n+// ConditionalNormal returns {nil, false} if there is a failure during the update.\n+// Mathematically this is impossible, but can occur with finite precision arithmetic.\n+func (n *Normal) ConditionalNormal(observed []int, values []float64, src *rand.Rand) (*Normal, bool) {\n+\tif len(observed) != len(values) {\n+\t\tpanic(\"norm: input slice length mismatch\")\n+\t}\n+\t// Recompute original sigma\n+\tsigma := mat64.NewSymDense(n.Dim(), nil)\n+\tsigma.FromCholesky(&n.chol)","path":"distmv/normal.go","position":24,"original_position":24,"commit_id":"b43fee7dbc2e14a0e60fbb526cb22b9a31b5824c","original_commit_id":"b43fee7dbc2e14a0e60fbb526cb22b9a31b5824c","user":{"login":"btracey","id":3680859,"avatar_url":"https://avatars.githubusercontent.com/u/3680859?v=3","gravatar_id":"","url":"https://api.github.com/users/btracey","html_url":"https://github.com/btracey","followers_url":"https://api.github.com/users/btracey/followers","following_url":"https://api.github.com/users/btracey/following{/other_user}","gists_url":"https://api.github.com/users/btracey/gists{/gist_id}","starred_url":"https://api.github.com/users/btracey/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/btracey/subscriptions","organizations_url":"https://api.github.com/users/btracey/orgs","repos_url":"https://api.github.com/users/btracey/repos","events_url":"https://api.github.com/users/btracey/events{/privacy}","received_events_url":"https://api.github.com/users/btracey/received_events","type":"User","site_admin":false},"body":"The main reason is that it's double the memory storage (for something that's already O(n^2)). I would imagine that Conditional and Marginal (where Sigma is also needed) are relatively uncommon operations, so I wasn't sure if normal users should pay the extra cost.  It will also be necssary if we add a Cov method, which may be much more common. Not clear what the right tradeoff is.","created_at":"2015-11-10T05:04:22Z","updated_at":"2015-11-10T05:04:22Z","html_url":"https://github.com/gonum/stat/pull/61#discussion_r44370603","pull_request_url":"https://api.github.com/repos/gonum/stat/pulls/61","_links":{"self":{"href":"https://api.github.com/repos/gonum/stat/pulls/comments/44370603"},"html":{"href":"https://github.com/gonum/stat/pull/61#discussion_r44370603"},"pull_request":{"href":"https://api.github.com/repos/gonum/stat/pulls/61"}}},"pull_request":{"url":"https://api.github.com/repos/gonum/stat/pulls/61","id":50213193,"html_url":"https://github.com/gonum/stat/pull/61","diff_url":"https://github.com/gonum/stat/pull/61.diff","patch_url":"https://github.com/gonum/stat/pull/61.patch","issue_url":"https://api.github.com/repos/gonum/stat/issues/61","number":61,"state":"open","locked":false,"title":"Add 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