Reorganize files

.gitignore

@@ -86,4 +86,5 @@ data/*
 # Ignore html files for now
 # TODO: Remove later
 *.html
-25_07_phd_defense/index_cache
+index_cache
+index_files

.vscode/settings.json

@@ -1,8 +1,8 @@
 {
   "files.autoSave": "off",
   "files.exclude": {
-    "**/.git": true,
+    "**/.git": false,
-    ".git": true
+    ".git": false
   },
   "todo-tree.tree.hideTreeWhenEmpty": false
 }

index.qmd

@@ -10,7 +10,7 @@ affiliations:
       name: University of Duisburg-Essen, House of Energy, Climate and Finance
 format:
     revealjs:
-        embed-resources: true
+        embed-resources: false
         footer: ""
         logo: assets/logos_combined.png
         theme: [default, sydney.scss, custom.scss]
@@ -808,65 +808,10 @@ We receive the constant solution for high values of $\lambda$ when setting $d=1$
 
 ## The Proposed CRPS-Learning Algorithm
 
-:::: {.columns}
+```{r, fig.align="left", echo=FALSE, out.width = "1000px", cache = TRUE}
+knitr::include_graphics("assets/crps_learning/algorithm_1.svg")
+```
 
-::: {.column width="48%"}
-
-**Initialization:**
-
-Array of expert predicitons: $\widehat{X}_{t,k,p}$
-
-Vector of Prediction targets: $Y_t$
-
-Starting Weights: $w_0=(w_{0,1},\ldots, w_{0,K})$,
-
-Penalization parameter: $\lambda\geq 0$
-
-B-spline and penalty matrices $B$ and $D$ on $\mathcal{P}= (p_1,\ldots,p_M)$
-
-Hat matrix: $$\mathcal{H} = B(B'B+ \lambda D'D)^{-1} B'$$
-
-Cumulative Regret: $R_{0,k} = 0$
-
-Range parameter: $E_{0,k}=0$
-
-:::
-
-::: {.column width="2%"}
-
-:::
-
-::: {.column width="48%"}
-
-**Core**:
-
-for(t in 1:T) { for(p in $\mathcal{P}$) {
-
-     $\widetilde{X}_{t,k}(p) = \sum_{k=1}^K w_{t-1,k,p} \widehat{X}_{t,k}(p)$ .grey[\# Prediction]
-
-     for(k in 1:K){
-
-       $r_{t,k,p} = \text{QL}_p^{\nabla}(\widehat{X}_{t,k}(p),Y_t) - \text{QL}_p^{\nabla}(\widetilde{X}_{t}(p),Y_t)$
-
-       $E_{t,k,p} = \max(E_{t-1,k,p}, |r_{t,k,p}|)$
-
-       $\eta_{t,k,p}=\min\left(1/2E_{t,k,p}, \sqrt{\log(K)/ \sum_{i=1}^t (r^2_{i, k,p})}\right)$
-
-       $R_{t,k,p} = R_{t-1,k,p} + \frac{1}{2} \left( r_{t,k,p}  \left( 1+ \eta_{t,k,p}  r_{t,k,p} \right) + 2E_{t,k,p}  \mathbb{1}(\eta_{t,k,p}r_{t,k,p} > \frac{1}{2}) \right)$
-
-       $w_{t,k,p} =  \eta_{t,k,p} \exp \left(-  \eta_{t,k,p}  R_{t,k,p} \right) w_{0,k,p} / \left( \frac{1}{K}  \sum_{k = 1}^K  \eta_{t,k,p} \exp \left( - \eta_{t,k,p}  R_{t,k,p}\right) \right)$
-
-   }.grey[\#k]}.grey[\#p]
-
-   for(k in 1:K){
-
-     $w_{t,k} = \mathcal{H} w_{t,k}(\mathcal{P})$ .grey[\# Smoothing]
-
-} .grey[\#k]} .grey[\#t]
-
-:::
-
-::::
 
 ## Simulation Study
 
@@ -3180,9 +3125,12 @@ Accounting for heteroscedasticity or stabilizing the variance via log transforma
 
 ::::
 
-## References
+## References 
+
+::: {.scrollable}
 
 ```{r refs1, echo=FALSE, results="asis"}
 PrintBibliography(my_bib, .opts = list(style = "text"))
 ```
 
+::::