Searched for: subject%3A%22probabilistic%255C+programming%22
(1 - 4 of 4)
document
Molhoek, Jord (author)
Many real-world problems fall in the category of sequential decision-making under uncertainty; Markov Decision Processes (MDPs) are a common method for modeling such problems. To solve an MDP, one could start from scratch or one could already have an idea of what good policies look like. Furthermore, there could be uncertainty in this idea. In...
master thesis 2024
document
Katona, Misha (author)
Through several contractions, stiff competition, and increasing passenger expectations, airports must evolve continually. One of the main avenues for this has been improving the efficiency of the security check- points, which are airports’ primary bottlenecks. Operational optimisation methods, such as resource and task scheduling are relatively...
master thesis 2024
document
Gardos Reid, Reuben (author)
Over 700 trains in the Netherlands are used daily for passenger transportation. Train operations involve tasks like parking, recombination, cleaning, and maintenance, which take place in shunting yards. The train unit shunting problem (TUSP) is a complex planning problem made more difficult by uncertainties such as delays. Most existing...
master thesis 2023
document
Jacobs, J. (author)
Abstract Probabilistic programming languages allow programmers to write down conditional probability distributions that represent statistical and machine learning models as programs that use observe statements. These programs are run by accumulating likelihood at each observe statement, and using the likelihood to steer random choices and...
journal article 2021
Searched for: subject%3A%22probabilistic%255C+programming%22
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