## 1 Introduction
It may provide fast predictions with non-linearities taken into account, overcoming the inaccuracies in linear analytical methods currently used in the early design stages [7]. Neural networks are poised to emerge as a pivotal tool in the initial phase of ship design, offering, among other capabilities, the estimation of crucial ship particulars [3]. A comprehensive examination of machine learning applications in sustainable ship design and operation is outlined in a study by [7]. Ref. [6] developed a series of artificial neural network (ANN) and regression equations to predict container ship dimensions, including the length between perpendiculars, breadth, draft, and side depth, utilizing deadweight, TEU capacity, and ship speed as input variables; ref. [8] demonstrated that augmenting the dataset with synthetic data and analyzing it using artificial neural networks (ANNs) can yield favorable outcomes concerning the main particulars of container ships. Employing artificial intelligence (AI) techniques, ref. [3] utilized multilayer perceptron and gradient-boosted trees for predicting key parameters of container ships. Ref. [2] applied non-linear regressions to ascertain the main dimensions, light ship characteristics, and dimensional relationships across various ship types. Furthermore, neural network (NN) methods have found application in diverse maritime domains, encompassing ship resistance prediction [9; 10; 11; 12], ship engine power [13; 14] and performance forecasting [15; 16; 17], ship hydrostatics [18], ship hydrodynamics, and motion prediction [19; 20; 21], as well as condition-based maintenance of machinery systems [22] and fault diagnosis [23]. The effectiveness of regression methods in accurately estimating both point and range estimates for parameters has been well-documented through research across various disciplines of science [24; 25]. However, in general, machine learning algorithms tend to replace traditional statistical methods [26]. Addressing the probabilistic dependency structure between multiple parameters necessitates a strategic approach. This strategy involves employing a learning algorithm to construct a neural network capable of answering diverse inquiries. The Bayesian method is adept at avoiding erroneous categorical decisions regarding conditional independencies, conducting model averaging for small datasets, managing missing data, and discerning between models [27]. Notably, its advantages include suitability for small and incomplete datasets, potential for structured learning, integration of diverse information sources, explicit treatment of uncertainty, and support for decision analysis and prompt responses. Leveraging these advantages, various Bayesian models have been demonstrated [28; 29; 30] for parameter identification and prediction of ship motions and maneuverability, contributing to the prediction of vessel hydrodynamics. Additionally, Bayesian networks find application in risk assessment [31; 32; 33; 34], accident scenario analysis [35; 36; 37], reliability analysis [38; 39], and fuel consumption analysis [40; 41] within the maritime domain. According to related studies, while traditional empirical methods retain value for certain aspects of ship design, the increasing complexity and evolving nature of modern ships necessitate the adoption of alternative approaches to ensure optimal performance, efficiency, and compliance with regulatory standards. Neural network (NN) methods, as one of these alternative approaches, are utilized across various disciplines within the maritime industry and are also applied in the ship design process. However, in the studies carried out to determine the main dimensions of the ship during the ship design phase, it was seen that the studies were concentrated on only a few cargo ship types such as container ships, and a specific study was required for a specific type of ship, harbor tugboats.