Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 3,242 papers for “Bayesian programming”

Memoryefficient dynamic programming for learning optimal Bayesian networks

On Malone, Changhe Yuan, Eric A. Hansen · 2011

We describe a memory-efficient implementation of a dynamic programming algorithm for learning the op-timal structure of a Bayesian network from training data. The algorithm leverages the layered structure of the dynamic programming graphs representing the re-cursive decomposition of the problem to reduce the memory requirements of the algorithm from O(n2n) to O(C(n, n/2)), where C(n, n/2) is the binomial coeffi-cient. Experimental results show that the approach runs up to an order of magnitude faster and scales to datasets with more variables than previous approaches.

Cited by 0

Debtor level collection operations using Bayesian dynamic programming

M.C. So, Christophe Mues, Adiel Teixeira de Almeida Filho et al. · Journal of the Operational Research Society · 2019

After a borrower defaults on their repayment obligations, collectors of unsecured consumer credit debt have a number of actions (e.g., telephone calls, formal letters,) they can take to secure some repayment of the debt. If these actions fail, collectors could seek legal proceedings. The operations management challenge in this setting is to decide which of these actions to take, how long to take them, and in what sequence to take them. Ideally, this collection policy should depend on how the defaulter has been performing during the collection process so far. In particular, it should take into account how many payments the defaulter has made under the current action, compared with how long that action has been tried. Other potential considerations aside, the objective of a collections policy typically is to maximize the recovery rate, i.e., the percentage of the defaulted debt that is recovered in the collections process. In this paper, we use a Bayesian Markov Decision Process (MDP) model to find an optimal policy of what action to undertake in the next period given the current information on the individual debtor’s repayment performance thus far. The proposed model will be empirically validated with data provided by a European bank’s in-house collections department. The model will be able to use by banks to decide their debt collection strategy.

Cited by 14

Affine Monads and Lazy Structures for Bayesian Programming

Swaraj Dash, Younesse Kaddar, Hugo Paquet et al. · arXiv (Cornell University) · 2022

We show that streams and lazy data structures are a natural idiom for programming with infinite-dimensional Bayesian methods such as Poisson processes, Gaussian processes, jump processes, Dirichlet processes, and Beta processes. The crucial semantic idea, inspired by developments in synthetic probability theory, is to work with two separate monads: an affine monad of probability, which supports laziness, and a commutative, non-affine monad of measures, which does not. (Affine means that $T(1)\cong 1$.) We show that the separation is important from a decidability perspective, and that the recent model of quasi-Borel spaces supports these two monads. To perform Bayesian inference with these examples, we introduce new inference methods that are specially adapted to laziness; they are proven correct by reference to the Metropolis-Hastings-Green method. Our theoretical development is implemented as a Haskell library, LazyPPL.

Cited by 0Open access

Safe Bayesian optimization across noise models via scenario programming

Tokmak, Abdullah, Schön, Thomas B., Baumann, Dominik · ArXiv.org · 2025

Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and safety guarantees. However, most safe BO algorithms assume homoscedastic sub-Gaussian measurement noise, an assumption that does not hold in many relevant applications. In this article, we propose a straightforward yet rigorous approach for safe BO across noise models, including homoscedastic sub-Gaussian and heteroscedastic heavy-tailed distributions. We provide a high-probability bound on the measurement noise via the scenario approach, integrate these bounds into high probability confidence intervals, and prove safety and optimality for our proposed safe BO algorithm. We deploy our algorithm in synthetic examples and in tuning a controller for the Franka Emika manipulator in simulation.

Cited by 0Open access

Integrated Bayesian Networks and Linear Programming for Decision Optimization

Assel Abdildayeva, Assem Shayakhmetova, Galymzhan Baurzhanuly Nurtugan · Mathematics · 2025

This paper develops a general BN → LP framework for decision optimization under complex, structured uncertainty. A Bayesian network encodes causal dependencies among drivers and yields posterior joint probabilities; a linear program then reads expected coefficients directly from BN marginals to optimize the objective under operational constraints with explicit risk control via chance constraints or small ambiguity sets centered at the BN posterior. This mapping avoids explicit scenario enumeration and separates feasibility from credibility, so extreme but implausible cases are down-weighted rather than dictating decisions. A farm-planning case with interacting factors (weather → disease → yield; demand ↔ price; input costs) demonstrates practical feasibility. Under matched risk control, the BN → LP approach maintains the target violation rate while avoiding the over-conservatism of flat robust optimization and the optimism of independence-based stochastic programming; it also circumvents the inner minimax machinery typical of distributionally robust optimization. Tractability is governed by BN inference over the decision-relevant ancestor subgraph; empirical scaling shows that Markov-blanket pruning, mutual-information screening of weak parents, and structured/low-rank CPDs yield orders-of-magnitude savings with negligible impact on the objective. A standardized, data-and-expert construction (Dirichlet smoothing) and a systematic sensitivity analysis identifies high-leverage parameters, while a receding-horizon DBN → LP extension supports online updates. The method brings the largest benefits when uncertainty is high-dimensional and coupled, and it converges to classical allocations when drivers are few and essentially independent.

Cited by 0Open access

Document processing with Bayesian network and agent-based programming

András Barta, István Vajk · 2005

Abstract:- This paper presents a document processing algorithm that is based on a Bayes network and a recursive orthogonal linear transformation. The system is extended to include agent-based programming in order to simplify the control procedures. The research also investigates the teaching procedure of the object library. The method is demonstrated on a document image processing problem, extracting the components of a circuit diagram.

Cited by 0

Bayesian differential programming for robust systems identification under uncertainty

Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris · arXiv (Cornell University) · 2020

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo. This allows us to efficiently infer posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods including nonlinear oscillators, predator-prey systems, chaotic dynamics and systems biology. Taken all together, our findings put forth a novel, flexible and robust workflow for data-driven model discovery under uncertainty.

Cited by 7Open access

LEARNING BAYESIAN NETWORKS STRUCTURES BASED ON EXTENDING EVOLUTIONARY PROGRAMMING

Xjao-Lin Li · 2004

This paper descrik a new data mining algorithm to learn Bayesian networks sbuctum based on an extending Evolutionary programming (EP) method and the Minimum Description Length (MDL) principle. Aiming at preventing and overcoming premature convergence, the algorithm combines the niche technology into the selection mechanism of EP. In addition, our algorithm, like some previous work, does not need to have a complete variable ordering as input. To evaluate the performance of our algorithm, we conduct a series of experiments and compare them with previous work based on genetic algorithms (GA). The experimental results illustrate that both quality of the solntions and computational time of our algorithm are superior.

Cited by 0

Safe Bayesian optimization across noise models via scenario programming

Tokmak, Abdullah, Schön, Thomas B., Baumann, Dominik · arXiv (Cornell University) · 2025

Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and safety guarantees. However, most safe BO algorithms assume homoscedastic sub-Gaussian measurement noise, an assumption that does not hold in many relevant applications. In this article, we propose a straightforward yet rigorous approach for safe BO across noise models, including homoscedastic sub-Gaussian and heteroscedastic heavy-tailed distributions. We provide a high-probability bound on the measurement noise via the scenario approach, integrate these bounds into high probability confidence intervals, and prove safety and optimality for our proposed safe BO algorithm. We deploy our algorithm in synthetic examples and in tuning a controller for the Franka Emika manipulator in simulation.

Cited by 0Open access

ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

Willie Neiswanger, Kirthevasan Kandasamy, Barnabás Póczos et al. · arXiv (Cornell University) · 2019

Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Bayesian optimization (BO), which leverages probabilistic models for this task. Most BO today uses Gaussian processes (GPs), or a few other surrogate models. However, there is a broad set of Bayesian modeling techniques that could be used to capture complex systems and reduce the number of queries in BO. Probabilistic programming languages (PPLs) are modern tools that allow for flexible model definition, prior specification, model composition, and automatic inference. In this paper, we develop ProBO, a BO procedure that uses only standard operations common to most PPLs. This allows a user to drop in a model built with an arbitrary PPL and use it directly in BO. We describe acquisition functions for ProBO, and strategies for efficiently optimizing these functions given complex models or costly inference procedures. Using existing PPLs, we implement new models to aid in a few challenging optimization settings, and demonstrate these on model hyperparameter and architecture search tasks.

Cited by 9Open access

Value of Seasonal Flow Forecasts in Bayesian Stochastic Programming

Young-Oh Kim, Richard N. Palmer · Journal of Water Resources Planning and Management · 1997

This paper presents a Bayesian Stochastic Dynamic Programming (BSDP) model to investigate the value of seasonal flow forecasts in hydropower generation. The proposed BSDP framework generates monthly operating policies for the Skagit Hydropower System (SHS), which supplies energy to the Seattle metropolitan area. The objective function maximizes the total benefits resulting from energy produced by the SHS and its interchange with the Bonneville Power Administration. The BSDP-derived operating policies for the SHS are simulated using historical monthly inflows, as well as seasonal flow forecasts during 60 years from January 1929 through December 1988. Performance of the BSDP model is compared with alternative stochastic dynamic programming models. To illustrate the potential advantage of using the seasonal flow forecasts and other hydrologic information, the sensitivity of SHS operation is evaluated by varying (1) the reservoir capacity; (2) the energy demand; and (3) the energy price. The simulation results demonstrate that including the seasonal forecasts is beneficial to SHS operation.

Cited by 113

Scaling Bayesian inference for generative models via probabilistic programming

João Loula Guimarães de Campos · DSpace@MIT (Massachusetts Institute of Technology) · 2025

This thesis addresses these challenges for the field of data science, developing probabilistic programming methods that enable rational AI agents in that domain. The work is organized into two parts: Part I introduces GenLM and Adaptive Weighted Rejection Sampling for translating natural language instructions into structured programs with both syntactic and semantic constraints, outperforming existing approaches across a number of domains. Part II develops Bayesian generative models for tabular data that can answer a wide range of questions, yield stable inferences across subpopulations of different sizes, and scale to hundreds of millions of rows on GPUs; as well as early work on Large Population Models that unify heterogeneous datasets. Together, these contributions provide first steps towards a unified framework for creating AI agents that can rationally formalize and answer questions about data.

Cited by 0Open access

An Embedded Implementation of Bayesian Network Robot Programming Methods

Matt Post · 2015

A wide variety of approaches exist for dealing with uncertainty in robotic reasoning, but relatively few consider the propagation of statistical information throughout an entire robotic system. The concept of Bayesian Robot Programming involves making decisions based on inference into probability distributions, but can be complex and difficult to implement due to the number of priors and random variables involved. In this work, we apply Bayesian network structures to a modified Bayesian programming paradigm to provide intuitive structure and simplify the programming process. The use of discrete random variables in the network can allow high inference speeds, and an efficient programming toolkit suitable for use on embedded platforms has been developed for use on mobile robots. A simple example of navigational reasoning for a small mobile robot is provided as an example of how such a network can be used for decisional programming.

Cited by 1Open access

Sequential Bayesian optimal experimental design via approximate dynamic programming

Xun Huan, Youssef Marzouk · arXiv (Cornell University) · 2016

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formulate the general sequential optimal experimental design (sOED) problem as a dynamic program. Batch and greedy designs are shown to result from special cases of this formulation. We then focus on sOED for parameter inference, adopting a Bayesian formulation with an information theoretic design objective. To make the problem tractable, we develop new numerical approaches for nonlinear design with continuous parameter, design, and observation spaces. We approximate the optimal policy by using backward induction with regression to construct and refine value function approximations in the dynamic program. The proposed algorithm iteratively generates trajectories via exploration and exploitation to improve approximation accuracy in frequently visited regions of the state space. Numerical results are verified against analytical solutions in a linear-Gaussian setting. Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.

Cited by 38Open access

Bayesian differential programming for robust systems identification under uncertainty

Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2020

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling. This allows an efficient inference of the posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods, including nonlinear oscillators, predator-prey systems and examples from systems biology. Taken together, our findings put forth a flexible and robust workflow for data-driven model discovery under uncertainty. All codes and data accompanying this article are available at https://bit.ly/34FOJMj.

Cited by 30Open access