THE ESSENCE OF BAYESIAN INFERENCE IN SHIPPING At its core, Bayesian inference in shipping is about updating probabilities of different freight rate models in light of new market evidence. Instead of choosing one model with absolute certainty, we assign probabilities to different models and adjust these probabilities as we gather more information from the market. For instance, rather than saying “I believe this freight rate model is true,” we might say, “I think this model has an 80% chance of accurately predicting rate movements, while these other models have smaller probabilities.” This nuanced approach allows for a more sophisticated understanding of shipping market dynamics.
THE POWER OF BAYES’ RULE IN FREIGHT MARKETS Bayes’ rule, the mathematical foundation of Bayesian inference, can be expressed as:
In the context of freight market modeling, we can interpret this as: A: The probability that a certain freight rate model is accurate
B: Newly observed evidence (e.g., recent rate movements or fixture reports)
A PRACTICAL EXAMPLE: THE TWO-URN MODEL IN SHIPPING To illustrate Bayesian inference in shipping, let’s consider a simple example with two urns representing different freight market conditions:
Urn 1
50% high-rate fixtures, 50% low-rate fixtures
Urn 2
25% high-rate fixtures, 75% low-rate fixtures
Imagine we observe a low-rate fixture without knowing which market condition it came from. Initially, we had no preference between the two conditions (50-50 chance). But after observing a low-rate fixture, how do our probabilities update? Using Bayes’ rule, we can calculate:
Our belief in Urn 1 (balanced market) has decreased to 40%, while our belief in Urn 2 (bearish market) has increased to 60%. This simple example demonstrates how Bayesian inference allows us to update our beliefs about market conditions based on new fixture information.
11 | ADMISI - The Ghost In The Machine | Q1 Edition 2025
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