typos

bgctw · bgctw · commit 105e369f9714 · 2025-08-19T15:57:17.000+02:00
diff --git a/dev/doubleMM.jl b/dev/doubleMM.jl
@@ -325,8 +325,8 @@ histogram(θsP)
     # resopt.objective
     # probo = prob3o = HybridProblem(prob2; ϕg = cpu_ca(ϕ).ϕg, θP = θP, ϕunc = cpu_ca(ϕ).unc)
 
-    solver_post2 = HybridPosteriorSovler(solver_post; n_MC = 30)
-    #solver_post2 = HybridPosteriorSovler(solver_post; n_MC = 3)
+    solver_post2 = HybridPosteriorSolver(solver_post; n_MC = 30)
+    #solver_post2 = HybridPosteriorSolver(solver_post; n_MC = 3)
     n_rep = 30
     n_batchf = n_site
     n_batchf = n_site ÷ 10
@@ -632,7 +632,7 @@ lineplot!(plt, 0, 1)
     set_default_AoGTheme!(;makie_config)
 
     using ColorBrewer: ColorBrewer
-    # two same colors for hmc anc hvi , additional for further unspecified labels
+    # two same colors for hmc and hvi , additional for further unspecified labels
     cDark2 = cgrad(ColorBrewer.palette("Dark2",3),3,categorical=true)
     #color_methods = vcat([k => col for (k, col) in zip([:hmc, :hvi], cDark2[1:2])], cDark2[3], Makie.wong_colors()[2:end]);
     cpal0 = Makie.wong_colors()
diff --git a/docs/src/explanation/theory_hvi.md b/docs/src/explanation/theory_hvi.md
@@ -89,7 +89,7 @@ the cholesky factors of the covariance and correlation matrices respectively. Th
 upper triangular matrices. 
 
 Since, the block-diagonal structure of the correlation matrix carries over to the cholesky 
-factor, $U_C$ is a block-diagnonal matrix of smaller cholesky factors. If HVI modeled the 
+factor, $U_C$ is a block-diagonal matrix of smaller cholesky factors. If HVI modeled the 
 depence between $\zeta_{Ms}$ and $\zeta_P$, the correlation matrhix would have an 
 additional block repeated in the first row and its transpose repeated in the first column 
 in $\Sigma$, leading to a cholesky factor $U_C$ having entries in all the rows. 
diff --git a/docs/src/problem.md b/docs/src/problem.md
@@ -13,7 +13,7 @@ For example we have soil organic matter process-model that predicts carbon stock
 different sites. We need to parameterize the unknown carbon use efficiency (CUE) of the soil
 microbial community that differs by site, but is hypothesized to correlate with climate variables
 and pedogenic factors, such as clay content.
-We apply a machine learnign model to estimate CUE and fit it end-to-end with other
+We apply a machine learning model to estimate CUE and fit it end-to-end with other
 parameters of the process-model to observed carbon stocks.
 In addition to the predicted CUE, we are interested in the uncertainty of CUE and its correlation with other parameters, such a the capacity of the soil minerals to bind carbon. 
 I.e. we are interetes in the entire posterior probability distribution of the model parameters.
diff --git a/docs/src/tutorials/basic_cpu.md b/docs/src/tutorials/basic_cpu.md
@@ -116,7 +116,7 @@ priors_dict = Dict{Symbol, Distribution}(
 
 ## Observations, model drivers and covariates
 
-The model paraemters are inverted using information on the
+The model parameters are inverted using information on the
 - observed data, `y_o`
 - its uncertainty, `y_unc`
 - known covariates across sites, `xM`
@@ -151,7 +151,7 @@ components S1 and S2 corresponding to the concentrations, for which outputs
 were observed.
 This allows notation `x.S1` in the PBM above.
 
-The `y_unc` becomes its meaning by the Liklihood-function to be specified with
+The `y_unc` becomes its meaning by the Likelihood-function to be specified with
 the problem below.
 
 ### Providing data in batches
diff --git a/docs/src/tutorials/basic_cpu.qmd b/docs/src/tutorials/basic_cpu.qmd
@@ -121,7 +121,7 @@ priors_dict = Dict{Symbol, Distribution}(
 
 ## Observations, model drivers and covariates
 
-The model paraemters are inverted using information on the 
+The model parameters are inverted using information on the 
 - observed data, `y_o`
 - its uncertainty, `y_unc`
 - known covariates across sites, `xM`
@@ -160,7 +160,7 @@ components S1 and S2 corresponding to the concentrations, for which outputs
 were observed.
 This allows notation `x.S1` in the PBM above.
 
-The `y_unc` becomes its meaning by the Liklihood-function to be specified with
+The `y_unc` becomes its meaning by the Likelihood-function to be specified with
 the problem below.
 
 ### Providing data in batches
diff --git a/docs/src/tutorials/how_to_guides/blocks_corr.qmd b/docs/src/tutorials/how_to_guides/blocks_corr.qmd
@@ -203,7 +203,7 @@ In each column of the model drivers there is a ComponentVector with
 components S1 and S2 corresponding to the concentrations, for which outputs
 were observed.
 
-The `y_unc` becomes its meaning by the Liklihood-function to be specified with
+The `y_unc` becomes its meaning by the Likelihood-function to be specified with
 the problem below.
 
 ### Providing data in Batches
diff --git a/docs/src/tutorials/how_to_guides/corr_site_global.qmd b/docs/src/tutorials/how_to_guides/corr_site_global.qmd
@@ -194,7 +194,7 @@ In each column of the model drivers there is a ComponentVector with
 components S1 and S2 corresponding to the concentrations, for which outputs
 were observed.
 
-The `y_unc` becomes its meaning by the Liklihood-function to be specified with
+The `y_unc` becomes its meaning by the Likelihood-function to be specified with
 the problem below.
 
 ### Providing data in Batches
diff --git a/docs/src/tutorials/inspect_results.md b/docs/src/tutorials/inspect_results.md
@@ -118,7 +118,7 @@ fig
 ![](inspect_results_files/figure-commonmark/cell-11-output-1.png)
 
 We see that *K*₁ across sites ranges from about 0.18 to 0.25, and that
-its estimated uncertainty is aobut 0.034, slightly decreasing with the
+its estimated uncertainty is about 0.034, slightly decreasing with the
 values of the parameter.
 
 ## Predictive Posterior
diff --git a/docs/src/tutorials/inspect_results.qmd b/docs/src/tutorials/inspect_results.qmd
@@ -124,7 +124,7 @@ fig
 ```
 
 We see that $K_1$ across sites ranges from about 0.18 to 0.25, and that
-its estimated uncertainty is aobut 0.034, slightly decreasing with the 
+its estimated uncertainty is about 0.034, slightly decreasing with the 
 values of the parameter.
 
 ## Predictive Posterior
diff --git a/src/AbstractHybridProblem.jl b/src/AbstractHybridProblem.jl
@@ -164,7 +164,7 @@ Return a DataLoader that provides a tuple of
 - `xP`: Iterator of process-model drivers, with one element per site
 - `y_o`: matrix of observations with added noise, with one column per site
 - `y_unc`: matrix `sizeof(y_o)` of uncertainty information 
-- `i_sites`: Vector of indices of sites in toal sitevector for the minibatch
+- `i_sites`: Vector of indices of sites in the minibatch
 """
 function get_hybridproblem_train_dataloader end
 
diff --git a/src/PBMApplicator.jl b/src/PBMApplicator.jl
@@ -47,7 +47,7 @@ Construct AbstractPBMApplicator from process-based model `fθ` that computes pre
 for a single site.
 The Applicator combines enclosed `θFix`, with provided `θMs` and `θP` and
 constructs a `ComponentVector` that can be indexed by 
-symbolic parameter names, correspondning to the templates provided during
+symbolic parameter names, corresponding to the templates provided during
 construction of the applicator.
 
 ## Arguments 
