Fix typos in 18_GP_inference.ipynb and 19_geostatistics.ipynb

Author: iamhaingo

18_GP_inference.ipynb

@@ -905,7 +905,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We have inferred the variance parameter successfully. Estimating lengthscale, espetially for less smooth kernels is harder. One more issue is the non-identifiability of the pair lengthscale-variance. Hence, if they both really *need* to be inferred, strong priors would be beneficial."
+        "We have inferred the variance parameter successfully. Estimating lengthscale, especially for less smooth kernels is harder. One more issue is the non-identifiability of the pair lengthscale-variance. Hence, if they both really *need* to be inferred, strong priors would be beneficial."
       ]
     },
     {

19_geostatistics.ipynb

@@ -35,7 +35,7 @@
         "\n",
         "Geostatistics is the subarea of spatial statstistics which works with geostatstitical data. It finds applications in various fields such as natural resource exploration (e.g., oil and gas reserves estimation), environmental monitoring (e.g., air and water quality assessment), agriculture (e.g., soil fertility mapping), and urban planning (e.g., land use analysis) and, of course, epidemiology (e.g., disease mapping). It offers powerful tools for spatial data analysis, decision-making, and resource management in both scientific research and practical applications.\n",
         "\n",
-        " <span style=\"color:orange\">Kriging</span> is a statistical interpolation technique used primarily in geostatistics. It is named after South African mining engineer DanielG. Krige, who developed the method in the 1950s. Kriging is employed to estimate the value of a variable at an <span style=\"color:orange\">unmeasured location</span>based on the values <span style=\"color:orange\">observed</span> at nearby locations.\n",
+        " <span style=\"color:orange\">Kriging</span> is a statistical interpolation technique used primarily in geostatistics. It is named after South African mining engineer DanielG. Krige, who developed the method in the 1950s. Kriging is employed to estimate the value of a variable at an <span style=\"color:orange\">unmeasured location</span> based on the values <span style=\"color:orange\">observed</span> at nearby locations.\n",
         "\n",
         "The basic idea behind kriging is to model the spatial correlation or spatial autocorrelation of the variable being studied. This means that kriging considers the spatial structure of the data. It assumes that nearby points are more similar than those farther away and uses this information to make predictions.\n",
         "\n",