# Conditional Dependencies and Position Fixing
## 9 Conclusions  
In this paper, I propose a new approach to imprecise data handling. I followed the inference computation scheme while the solving the fixed problem of the nautical solution. Raw experimental instances, which were available with statistically sufficient counts, were assumed to be the initial source of the knowledge regarding the behaviour of the available system indications. Modern applications include the learning phase, while the necessary elements are extracted for further usage. In the proposed approach, the conditional dependencies are discovered. The appropriate diagrams take the form of converted histograms, and they are constructed in one of the first processing steps. The evaluation of the construct reliability is an important issue. In this study, I considered the uncertainty regarding the vertical and horizontal layouts of the histogram. The measures refer to a variety of bin heights and widths, which enabled the estimation of the overall uncertainty. The doubtfulness and shape of the membership functions are mutually dependent. Both were main factors in the shape of the converted histograms, and they are the conditional dependencies in adequate diagrams. Making a fix is an example of the problem engaging distorted data, which requires additional processing. The normalization introduces uniform probability density distributions. The relative initial vector is constructed to obtain the required hierarchy and subsequent probability assignments. Their combination leads to the solution to the problem.  
The proposed scheme for solving problems that involve distorted data includes several steps:  
1. The recording of the statistically justified number of instances for the involved sources of random data;
2. An exploration of the stored sets to mine the embedded uncertainty. The suggested solution involves histograms, and I present a method for their evaluation in the paper;
3. The creation of a ranking regarding the included doubtfulness within the available evidence items;  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(P(H_{i}\mid\boldsymbol{o_{1}})\)2  & \(P(H_{i}\mid\boldsymbol{o_{3}})\) & \(P(H_{i}\mid\boldsymbol{o_{4}})\) & \(\boldsymbol{m(H_{i})}\) \\ \hline \(H_{1}\)1  & 0.752 & 0.367 & 0.000 & 0.000 & 0.472 \\ \(H_{2}\)1  & 0.711 & 0.785 & 0.865 & 0.711 & 0.377 \\ \(H_{3}\)1  & 0.505 & 0.557 & 0.434 & 0.606 & 0.112 \\ \(H_{4}\)1  & 0.423 & 0.573 & 0.687 & 0.675 & 0.119 \\ \hline \hline \(\Theta_{Ri}(\Theta_{F})\) & 0.03 & 0.00 & 0.24 & 0.21 & (0.00) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected features for example pool of four histograms.  
Figure 7: Four pieces of evidence with four hypothesis points (\(o_{i}\) is the \(i\)-th piece of evidence; \(H_{i}\) is the \(i\)-th hypothesis point): (**a**) evidence instance dispersions; (**b**) their converted histograms.  
The obtainment of locally injective density distribution diagrams and the consideration of them as conditional dependencies, given the uncertainty-dependent membership functions and bin-to-bin additive method;
* The use of the diagrams to upgrade the simple belief assignments of the MTE. The results of their associations include the measures that indicate the solution.  
This research was funded by Gdynia Maritime University, Faculty of Management and Quality Science grant number WZNJ/2022/PZ/03. The APC was funded by WZNJ/2022/PZ/03.  
Not applicable.  
The author declares no conflict of interest.