Fix some typos (#1974)

Signed-off-by: Alexander Seiler <seileralex@gmail.com>
TuringLang · Apr 4, 2023 · 3e71a76 · 3e71a76
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diff --git a/docs/src/contributing/style-guide.md b/docs/src/contributing/style-guide.md
@@ -454,7 +454,7 @@ When referencing Julia in documentation note that "Julia" refers to the programm
 
 ```julia
 
-# A commment
+# A comment
 code
 
 # Another comment

diff --git a/docs/src/for-developers/interface.md b/docs/src/for-developers/interface.md
@@ -61,7 +61,7 @@ The full code for this implementation is housed in
 
 ### Imports
 
-Let's begin by importing the relevant libraries. We'll import `AbstracMCMC`, which contains
+Let's begin by importing the relevant libraries. We'll import `AbstractMCMC`, which contains
 the interface framework we'll fill out. We also need `Distributions` and `Random`.
 
 ```julia

diff --git a/docs/src/for-developers/variational_inference.md b/docs/src/for-developers/variational_inference.md
@@ -242,7 +242,7 @@ This means that if we have a differentiable bijection ``f: \mathrm{supp} \left(
 \mathbb{P}\_p(x \in A) = \int\_{f^{-1}(A)} p \left(f^{-1}(y) \right) \ \left| \det \mathcal{J}\_{f^{-1}}(y) \right| \mathrm{d}y,
 ```
 
-where ``\mathcal{J}_{f^{-1}}(x)`` denotes the jacobian of ``f^{-1}`` evaluted at ``x``. Observe that this defines a probability distribution
+where ``\mathcal{J}_{f^{-1}}(x)`` denotes the jacobian of ``f^{-1}`` evaluated at ``x``. Observe that this defines a probability distribution
 
 ```math
 \mathbb{P}\_{\tilde{p}}\left(y \in f^{-1}(A) \right) = \int\_{f^{-1}(A)} \tilde{p}(y) \mathrm{d}y,

diff --git a/src/inference/hmc.jl b/src/inference/hmc.jl
@@ -288,7 +288,7 @@ Arguments:
 - `n_adapts::Int` : Numbers of samples to use for adaptation.
 - `δ::Float64` : Target acceptance rate. 65% is often recommended.
 - `λ::Float64` : Target leapfrog length.
-- `ϵ::Float64=0.0` : Inital step size; 0 means automatically search by Turing.
+- `ϵ::Float64=0.0` : Initial step size; 0 means automatically search by Turing.
 
 For more information, please view the following paper ([arXiv link](https://arxiv.org/abs/1111.4246)):
 
@@ -356,7 +356,7 @@ Arguments:
 - `δ::Float64` : Target acceptance rate for dual averaging.
 - `max_depth::Int` : Maximum doubling tree depth.
 - `Δ_max::Float64` : Maximum divergence during doubling tree.
-- `init_ϵ::Float64` : Inital step size; 0 means automatically searching using a heuristic procedure.
+- `init_ϵ::Float64` : Initial step size; 0 means automatically searching using a heuristic procedure.
 
 """
 struct NUTS{AD,space,metricT<:AHMC.AbstractMetric} <: AdaptiveHamiltonian{AD}

diff --git a/test/inference/ess.jl b/test/inference/ess.jl
@@ -57,7 +57,7 @@
 
         # Different "equivalent" models.
         # NOTE: Because `ESS` only supports "single" variables with
-        # Guassian priors, we restrict ourselves to this subspace by conditioning
+        # Gaussian priors, we restrict ourselves to this subspace by conditioning
         # on the non-Gaussian variables in `DEMO_MODELS`.
         models_conditioned = map(DynamicPPL.TestUtils.DEMO_MODELS) do model
             # Condition on the non-Gaussian random variables.

diff --git a/test/inference/hmc.jl b/test/inference/hmc.jl
@@ -20,7 +20,7 @@
 
         check_numerical(chain, [:p], [10/14], atol=0.1)
     end
-    @numerical_testset "contrained simplex" begin
+    @numerical_testset "constrained simplex" begin
         obs12 = [1,2,1,2,2,2,2,2,2,2]
 
         @model function constrained_simplex_test(obs12)

diff --git a/test/stdlib/distributions.jl b/test/stdlib/distributions.jl
@@ -39,7 +39,7 @@
         multi_dim = 4
         # 1. UnivariateDistribution
         # NOTE: Noncentral distributions are commented out because of
-        #       AD imcompatibility of their logpdf functions
+        #       AD incompatibility of their logpdf functions
         dist_uni = [
             Arcsine(1, 3),
             Beta(2, 1),

diff --git a/test/test_utils/random_measure_utils.jl b/test/test_utils/random_measure_utils.jl
@@ -4,12 +4,12 @@ function compute_log_joint(observations, partition, tau0, tau1, sigma, theta)
   prob = k*log(sigma) + lgamma(theta) + lgamma(theta/sigma + k) - lgamma(theta/sigma) - lgamma(theta + n)
   for cluster in partition
     prob += lgamma(length(cluster) - sigma) - lgamma(1 - sigma)
-    prob += compute_log_conditonal_observations(observations, cluster, tau0, tau1)
+    prob += compute_log_conditional_observations(observations, cluster, tau0, tau1)
   end
   prob
 end
 
-function compute_log_conditonal_observations(observations, cluster, tau0, tau1)
+function compute_log_conditional_observations(observations, cluster, tau0, tau1)
   nl = length(cluster)
   prob = (nl/2)*log(tau1) - (nl/2)*log(2*pi) + 0.5*log(tau0) + 0.5*log(tau0+nl)
   prob += -tau1/2*(sum(observations)) + 0.5*(tau0*mu_0+tau1*sum(observations[cluster]))^2/(tau0+nl*tau1)