diff --git a/R/forge.R b/R/forge.R
index cfed28ce..2ac8705d 100644
--- a/R/forge.R
+++ b/R/forge.R
@@ -106,9 +106,7 @@ forge <- function(
   }
   omega <- omega[wt > 0]
   
-  if (nrow(omega) == 0) {
-    stop("All leaves have zero likelihood.")
-  } else if (nrow(omega) == 1) {
+  if (nrow(omega) == 1) {
     draws <- omega[, .(f_idx)]
   } else {
     # Draw random leaves with probability proportional to weight
diff --git a/R/utils.R b/R/utils.R
index 03770de5..4d8523d8 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -235,8 +235,13 @@ leaf_posterior <- function(params, evidence) {
   , by = f_idx]
   
   # Normalize, export
-  
   out <- unique(psi[wt > 0, .(f_idx, wt)])
+  if (nrow(out) == 0) {
+    # If all leaves have zero weight, choose one randomly
+    warning("All leaves have zero likelihood. This is probably because evidence contains an (almost) impossible combination. For categorical data, consider setting alpha>0 in forde().")
+    out <- unique(psi[, .(f_idx)])
+    out[, wt := 1]
+  }
   out[, wt := (wt / max(wt, na.rm = T))^(nrow(evidence) + 1)][wt > 0, wt := wt / sum(wt)]
   return(out[])
 }
@@ -256,7 +261,7 @@ leaf_posterior <- function(params, evidence) {
 post_x <- function(x, params) {
   
   # To avoid data.table check issues
-  variable <- NULL
+  variable <- val <- NULL
   
   # Order, classify features
   meta_tmp <- params$meta[variable %in% colnames(x)]
diff --git a/man/post_x.Rd b/man/post_x.Rd
index eafce015..32c072a9 100644
--- a/man/post_x.Rd
+++ b/man/post_x.Rd
@@ -12,5 +12,5 @@ post_x(x, params)
 \item{params}{Circuit parameters learned via \code{\link{forde}}.}
 }
 \description{
-This function prepares output data for map and forge.
+This function prepares output data for forge.
 }