46 research outputs found
ArviZ a unified library for exploratory analysis of Bayesian models in Python
ArviZ is a Python package for exploratory analysis of Bayesian models. ArviZ aims to be a package that integrates seamlessly with established probabilistic programming languages like PyStan, PyMC, Edward, emcee, Pyro and easily integrated with novel or bespoke Bayesian analyses. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ library is to make it easy to process and analyze the results from the Bayesian models.Fil: Kumar, Ravin. No especifĂca;Fil: Carroll, Colin. No especifĂca;Fil: Hartikainen, Ari. Aalto University; FinlandiaFil: MartĂn, Osvaldo Antonio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias FĂsico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentin
Bayesian additive regression trees for probabilistic programming
Bayesian additive regression trees (BART) is a non-parametric method to
approximate functions. It is a black-box method based on the sum of many trees
where priors are used to regularize inference, mainly by restricting trees'
learning capacity so that no individual tree is able to explain the data, but
rather the sum of trees. We discuss BART in the context of probabilistic
programming languages (PPL), i.e., we present BART as a primitive that can be
used as a component of a probabilistic model rather than as a standalone model.
Specifically, we introduce the Python library PyMC-BART, which works by
extending PyMC, a library for probabilistic programming. We showcase a few
examples of models that can be built using PyMC-BART, discuss recommendations
for the selection of hyperparameters, and finally, we close with limitations of
our implementation and future directions for improvement.Comment: 22 pages, 17 figure
Increasing interpretability of Bayesian probabilistic programming models through interactive visualizations
Bayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling. However, the results of Bayesian inference are challenging for users to interpret in tasks like decision-making under uncertainty or model refinement. Decision-makers need simultaneous insight into both the model’s structure and its predictions, including uncertainty in inferred parameters. This enables better assessment of the risk overall possible outcomes compatible with observations and thus more informed decisions. To support this, we see a need for visualization tools that make probabilistic programs interpretable to reveal the interdependencies in probabilistic models and their inherent uncertainty. We propose the automatic transformation of Bayesian probabilistic models, expressed in a probabilistic programming language, into an interactive graphical representation of the model’s structure at varying levels of granularity, with seamless integration of uncertainty visualization. This interactive graphical representation supports the exploration of the prior and posterior distribution of MCMC samples. The interpretability of Bayesian probabilistic programming models is enhanced through the interactive probabilistic models explorer, which provides human users with more informative, transparent, and explainable probabilistic models. We present a concrete implementation that translates probabilistic programs to interactive graphical representation and show illustrative examples for a variety of Bayesian probabilistic models
Bayesian additive regression trees for probabilistic programming
Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees’ learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPLs), specifically we introduce a BART implementation extending PyMC, a Python library for probabilistic programming. We present a few examples of models that can be built using this probabilistic programming-oriented version of BART, discuss recommendations for sample diagnostics and selection of model hyperparameters, and finally we close with limitations of the current approach and future extensions.Fil: Quiroga Andiñach, Miriana Esther. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias FĂsico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Garay, Pablo Germán. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias FĂsico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Alonso, Juan Manuel. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias FĂsico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Loyola, Juan Martin. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias FĂsico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: MartĂn, Osvaldo Antonio. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias FĂsico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentin
Analysis of Heterogeneous Data Sources for Veterinary Syndromic Surveillance to Improve Public Health Response and Aid Decision Making
The standard technique of implementing veterinary syndromic surveillance (VSyS) is the detection of temporal or spatial anomalies in the occurrence of health incidents above a set threshold in an observed population using the Frequentist modelling approach. Most implementation of this technique also requires the removal of historical outbreaks from the datasets to construct baselines. Unfortunately, some challenges exist, such as data scarcity, delayed reporting of health incidents, and variable data availability from sources, which make the VSyS implementation and alarm interpretation difficult, particularly when quantifying surveillance risk with associated uncertainties. This problem indicates that alternate or improved techniques are required to interpret alarms when incorporating uncertainties and previous knowledge of health incidents into the model to inform decision-making. Such methods must be capable of retaining historical outbreaks to assess surveillance risk.
In this research work, the Stochastic Quantitative Risk Assessment (SQRA) model was proposed and developed for detecting and quantifying the risk of disease outbreaks with associated uncertainties using the Bayesian probabilistic approach in PyMC3. A systematic and comparative evaluation of the available techniques was used to select the most appropriate method and software packages based on flexibility, efficiency, usability, ability to retain historical outbreaks, and the ease of developing a model in Python. The social media datasets (Twitter) were first applied to infer a possible disease outbreak incident with associated uncertainties. Then, the inferences were subsequently updated using datasets from the clinical and other healthcare sources to reduce uncertainties in the model and validate the outbreak. Therefore, the proposed SQRA model demonstrates an approach that uses the successive refinement of analysis of different data streams to define a changepoint signalling a disease outbreak.
The SQRA model was tested and validated to show the method's effectiveness and reliability for differentiating and identifying risk regions with corresponding changepoints to interpret an ongoing disease outbreak incident. This demonstrates that a technique such as the SQRA method obtained through this research may aid in overcoming some of the difficulties identified in VSyS, such as data scarcity, delayed reporting, and variable availability of data from sources, ultimately contributing to science and practice
Self-damping of Optical Ground Wire Cables: A Bayesian Approach
The empirical Power Law model has a long usage history in cable self-damping studies, and several types of research have been done to characterize its parameters for various types of cables. In this work, a novel Bayesian model calibration framework is proposed and applied to study self-damping Optical Ground Wire (OPGW) cables. This technique then combines experimental and statistical approaches to obtain the confidence intervals for each parameter and characterize the different regions where the model presents other behaviors. The results enable a better calibration of the model's parameters and agree with the trends already set in the literature. They also provide a new understanding of the model and estimate different uncertainties its application enticesFil: Campos, Damián Federico. Universidad Nacional del Comahue. Facultad de IngenierĂa; ArgentinaFil: Löser, Enrique Eduardo. Universidad Nacional del Comahue. Facultad de IngenierĂa; ArgentinaFil: Piovan, Marcelo Tulio. Universidad TecnolĂłgica Nacional. Facultad Regional BahĂa Blanca; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca; Argentin
Testing -Free f(Q) Cosmology
We study a model of Symmetric Teleparallel gravity that is able to account
for the current accelerated expansion of the universe without the need for dark
energy component. We investigate this model by making use of dynamical system
analysis techniques to identify the regions of the parameter space with viable
cosmologies and constrain it using type Ia supernova (SnIa), cosmic microwave
background (CMB) data and make forecasts using standard siren (SS) events. We
conclude that this model is disfavored with respect to CDM and
forthcoming standard siren events can be decisive in testing the viability of
the model.Comment: 9 pages, 3 figure