Escape backslashes in docstrings

src/rules.jl

@@ -53,7 +53,7 @@ backward(method::BFFG, k, q::Leaf; kargs...) = backward(method, k, q[]; kargs...
     backward(::BFFG, k::Union{AffineGaussianKernel,LinearGaussianKernel}, q::WGaussian{(:F,:Γ,:c)};
 
 For a Markov kernel `k` of the form `x ↦ N(Bx + β, Q)` this function computes 
-`x ↦ k q = ∫ q(y) pdf(N(Bx + β, Q), y) dy` in the form `q0(y) = exp(c0)⋅pdf(N(Γ0\F0, inv(Γ0)), y)`.
+`x ↦ k q = ∫ q(y) pdf(N(Bx + β, Q), y) dy` in the form `q0(y) = exp(c0)⋅pdf(N(Γ0 \\ F0, inv(Γ0)), y)`.
 Requires invertibility of `Γ`.
 
 
@@ -65,7 +65,7 @@ Returns a message objects for forward guiding and `q0`.
 Arguments:  
 
 * `k` a kernel such that `Y ~ N(B*x + β, Q)`
-* `q::WGaussian{(:F,:Γ,:c)}` is the (unnormalized) density q(y) = exp(c)⋅pdf(N(Γ\F, inv(Γ)), y)`
+* `q::WGaussian{(:F,:Γ,:c)}` is the (unnormalized) density q(y) = exp(c)⋅pdf(N(Γ \\ F, inv(Γ)), y)`
 """
 function backward(::BFFG, k::Union{AffineGaussianKernel,LinearGaussianKernel}, q::WGaussian{(:F,:Γ,:c)}; unfused=false)
     @unpack F, Γ, c = q
@@ -90,7 +90,7 @@ end
     backward(::BFFG, k::Union{AffineGaussianKernel,LinearGaussianKernel}, y; unfused=false)
  
 For a Markov kernel `k` of the form `x ↦ N(Bx + β, Q)` this function computes the function
-`x ↦ pdf(N(Bx + β, Q), y)` and returns it as a `WGaussian` in the form exp(c0)⋅pdf(N(Γ0\F0, inv(Γ0)), y)`
+`x ↦ pdf(N(Bx + β, Q), y)` and returns it as a `WGaussian` in the form exp(c0)⋅pdf(N(Γ0 \\ F0, inv(Γ0)), y)`
 wrapped in a `Leaf` object. 
 
 If `unfused=true` avoid a call to `logdet(B)`. This function is supposed to be called with observations 
@@ -228,7 +228,7 @@ end
     backward(::BFFG, ::Copy, a::Union{Leaf{<:WGaussian{(:F,:Γ,:c)}}, WGaussian{(:F,:Γ,:c)}}, args...)
    
 For a Markov kernel `k::Copy` that represents the deterministic function `x ↦ Dirac((x, ..., x))` 
-this function computes the corresponding pullback `k(h1, ..., hn) = h1(x)⋅...⋅hn(x)` and returns it as a `WGaussian` in the form exp(c0)⋅pdf(N(Γ0\F0, inv(Γ0)), y)`. 
+this function computes the corresponding pullback `k(h1, ..., hn) = h1(x)⋅...⋅hn(x)` and returns it as a `WGaussian` in the form exp(c0)⋅pdf(N(Γ0 \\ F0, inv(Γ0)), y)`. 
 From a Bayesian perspective, this performs *fusion* of the information about a value `x` given in form 
 of a tuple of unormalizes densities. If an argument is wrapped in a `Leaf` object, do the right thing.