Probabilistic Model Checking Taken by Storm

• URL: http://arxiv.org/abs/2603.15559v1
• Date: Mon, 16 Mar 2026 17:22:21 GMT
• Title: Probabilistic Model Checking Taken by Storm
• Abstract summary: This tutorial focuses on Markov decision processes (MDP), which are popular in a variety of fields.<n>Storm is a decade-old model checker that excels in performance and a rich Python-based ecosystem.
• Score: 5.706572558369409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This tutorial paper presents a hands-on perspective on probabilistic model checking with the Storm model checker. Storm is a decade-old model checker that excels in performance and a rich Python-based ecosystem, which makes it easy to integrate in various workflows. This tutorial focuses on Markov decision processes (MDP), which are popular in a variety of fields. It demonstrates the basic workflow, from Python-based modeling, model checking with a variety of properties, to the extraction of policies. Further, it showcases the support for recent topics that focus on different types of uncertainty, such as interval MDP and POMDP, and the ability to quickly implement simple algorithms on top of existing data structures.

Related papers

• Deep Multivariate Models with Parametric Conditionals [47.20275199636936]
  We consider deep multivariate models for heterogeneous collections of random variables.<n>We propose to represent the joint probability distribution by means of conditional probability distributions for each group of variables conditioned on the rest.<n>Their learning can be approached as training a parametrised Markov chain kernel by maximising the data likelihood of its limiting distribution.
  arXiv  Detail & Related papers  (2026-02-02T11:01:48Z)
• Tsururu: A Python-based Time Series Forecasting Strategies Library [37.84139339249633]
  Tsururu is a Python library that bridges SoTA research and industry.<n>It enables flexible combinations of global and multivariate approaches and multi-step-ahead forecasting strategies.
  arXiv  Detail & Related papers  (2025-09-19T10:26:00Z)
• Intention-Conditioned Flow Occupancy Models [80.42634994902858]
  Large-scale pre-training has fundamentally changed how machine learning research is done today.<n>Applying this same framework to reinforcement learning is appealing because it offers compelling avenues for addressing core challenges in RL.<n>Recent advances in generative AI have provided new tools for modeling highly complex distributions.
  arXiv  Detail & Related papers  (2025-06-10T15:27:46Z)
• Predicting the Performance of Black-box LLMs through Self-Queries [60.87193950962585]
  Large language models (LLMs) are increasingly relied on in AI systems, predicting when they make mistakes is crucial.<n>In this paper, we extract features of LLMs in a black-box manner by using follow-up prompts and taking the probabilities of different responses as representations.<n>We demonstrate that training a linear model on these low-dimensional representations produces reliable predictors of model performance at the instance level.
  arXiv  Detail & Related papers  (2025-01-02T22:26:54Z)
• BayesBlend: Easy Model Blending using Pseudo-Bayesian Model Averaging, Stacking and Hierarchical Stacking in Python [0.0]
  We introduce the BayesBlend Python package to estimate weights and blend multiple (Bayesian) models' predictive distributions. BayesBlend implements pseudo-Bayesian model averaging, stacking and, uniquely, hierarchical Bayesian stacking to estimate model weights. We demonstrate the usage of BayesBlend with examples of insurance loss modeling.
  arXiv  Detail & Related papers  (2024-04-30T19:15:33Z)
• NoMoPy: Noise Modeling in Python [0.0]
  NoMoPy is a code for fitting, analyzing, and generating noise modeled as a hidden Markov model (HMM) Here, we describe in detail functionality implemented in NoMoPy and provide examples of its use and performance on example problems.
  arXiv  Detail & Related papers  (2023-10-31T18:52:05Z)
• Predictable MDP Abstraction for Unsupervised Model-Based RL [93.91375268580806]
  We propose predictable MDP abstraction (PMA) Instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space. We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches.
  arXiv  Detail & Related papers  (2023-02-08T07:37:51Z)
• PAMI: partition input and aggregate outputs for model interpretation [69.42924964776766]
  In this study, a simple yet effective visualization framework called PAMI is proposed based on the observation that deep learning models often aggregate features from local regions for model predictions. The basic idea is to mask majority of the input and use the corresponding model output as the relative contribution of the preserved input part to the original model prediction. Extensive experiments on multiple tasks confirm the proposed method performs better than existing visualization approaches in more precisely finding class-specific input regions.
  arXiv  Detail & Related papers  (2023-02-07T08:48:34Z)
• DeeProb-kit: a Python Library for Deep Probabilistic Modelling [0.0]
  DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) It includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs.
  arXiv  Detail & Related papers  (2022-12-08T17:02:16Z)
• Sampling from Arbitrary Functions via PSD Models [55.41644538483948]
  We take a two-step approach by first modeling the probability distribution and then sampling from that model. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models.
  arXiv  Detail & Related papers  (2021-10-20T12:25:22Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.