Use logcosh

Co-authored-by: David Widmann <devmotion@users.noreply.github.com>

src/bijectors/corr.jl

@@ -353,7 +353,7 @@ function _inv_link_chol_lkj(Y::AbstractMatrix)
         for i in 1:(j - 1)
             z = tanh(Y[i, j])
             W[i, j] = z * exp(log_remainder)
-            log_remainder += IrrationalConstants.logtwo + Y[i, j] - LogExpFunctions.log1pexp(2 * Y[i, j])
+            log_remainder -= LogExpFunctions.logcosh(Y[i, j])
             logJ += log_remainder
         end
         logJ += log_remainder
@@ -380,7 +380,7 @@ function _inv_link_chol_lkj(y::AbstractVector)
         for i in 1:(j - 1)
             z = tanh(y[idx])
             W[i, j] = z * exp(log_remainder)
-            log_remainder += IrrationalConstants.logtwo + y[idx] - LogExpFunctions.log1pexp(2 * y[idx])
+            log_remainder -= LogExpFunctions.logcosh(y[idx])
             logJ += log_remainder
             idx += 1
         end
@@ -460,13 +460,8 @@ function _logabsdetjac_inv_corr(Y::AbstractMatrix)
     K = LinearAlgebra.checksquare(Y)
 
     result = float(zero(eltype(Y)))
-    for j in 2:K, i in 1:(j - 1)
-        @inbounds abs_y_i_j = abs(Y[i, j])
-        result +=
-            (K - i + 1) * (
-                IrrationalConstants.logtwo -
-                (abs_y_i_j + LogExpFunctions.log1pexp(-2 * abs_y_i_j))
-            )
+    @inbounds for j in 2:K, i in 1:(j - 1)
+        result += (K - i + 1) * (-LogExpFunctions.logcosh(Y[i, j]))
     end
     return result
 end
@@ -495,7 +490,7 @@ function _logabsdetjac_inv_chol(y::AbstractVector)
     @inbounds for j in 2:K
         tmp = zero(result)
         for _ in 1:(j - 1)
-            logz = 2 * (IrrationalConstants.logtwo + y[idx] - LogExpFunctions.log1pexp(2 * y[idx]))
+            logz = -2 * LogExpFunctions.logcosh(y[idx])
             result += logz + (tmp / 2)
             tmp += logz
             idx += 1