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Bayesian causal inference via probabilistic program synthesis [article]

Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka
2019 arXiv   pre-print
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions.  ...  This approach also enables the use of general-purpose inference machinery for probabilistic programs to infer probable causal structures and parameters from data.  ...  Figure 5 :Figure 6 : 56 Gen implementation of causal inference via Bayesian synthesis.  ... 
arXiv:1910.14124v1 fatcat:ywbfuyq4cjfzhjrpvqf4tqjhbi

Bayesian synthesis of probabilistic programs for automatic data modeling

Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka
2019 Proceedings of the ACM on Programming Languages (PACMPL)  
These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic  ...  We provide a precise formulation of Bayesian synthesis for automatic data modeling that identifies sufficient conditions for the resulting synthesis procedure to be sound.  ...  ACKNOWLEDGMENTS This research was supported by the DARPA SD2 program (contract FA8750-17-C-0239); grants from the MIT Media Lab, the Harvard Berkman Klein Center Ethics and Governance of AI Fund, and the  ... 
doi:10.1145/3290350 fatcat:xemazron3rg65nvmab2rdcgyei

Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation

Francesco De Pretis, Jürgen Landes, William Peden
2021 Journal of Evaluation In Clinical Practice  
While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest.  ...  E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations.  ...  Conceptually, indicators of causality are testable (probabilistic) consequences of the causal hypothesis.  ... 
doi:10.1111/jep.13542 pmid:33569874 fatcat:7gz33odnuvcmjdahfjfxrget4m

Building fast Bayesian computing machines out of intentionally stochastic, digital parts [article]

Vikash Mansinghka, Eric Jonas
2014 arXiv   pre-print
We evaluate circuits for depth and motion perception, perceptual learning and causal reasoning, each performing inference over 10,000+ latent variables in real time - a 1,000x speed advantage over commodity  ...  But Bayesian inference, which underpins many computational models of perception and cognition, appears computationally challenging even given modern transistor speeds and energy budgets.  ...  probabilistic programming language 19, 20 .  ... 
arXiv:1402.4914v1 fatcat:mnjmxywzyrgo5avrttcvsxosri

How to Grow a Mind: Statistics, Structure, and Abstraction

J. B. Tenenbaum, C. Kemp, T. L. Griffiths, N. D. Goodman
2011 Science  
In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it?  ...  Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought  ...  and probabilistic programming languages (67) .  ... 
doi:10.1126/science.1192788 pmid:21393536 fatcat:sh4diud5l5g6hkdw7u2eptlpou

Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains [article]

Vaishak Belle
2020 arXiv   pre-print
The idea is this: given a Bayesian network, a relational Bayesian network, a factor graph, or a probabilistic program [84] , one considers an encoding of the formalism as a weighted propositional theory  ...  learning: the problem of inference, including evaluating the partition function (or conditional probabilities) of a probabilistic graphical model such as a Bayesian network.  ... 
arXiv:2006.08480v1 fatcat:d23e4d6kcfbtjduztfr6y4536a

Υ-DB: A system for data-driven hypothesis management and analytics [article]

Bernardo Gonçalves, Frederico C. Silva, Fabio Porto
2014 arXiv   pre-print
The Υ-DB system addresses those challenges throughout a design-by-synthesis pipeline that defines its architecture.  ...  The vision of Υ-DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and  ...  Therefore Bayesian inference is applied for normal mean with a discrete prior [4] .  ... 
arXiv:1411.7419v1 fatcat:gysa3rtldnfkrec4clyxlexcca

Information Structures for Causally Explainable Decisions

Louis Anthony Cox
2021 Entropy  
This paper reviews how these and related concepts can be used to identify probabilistic causal dependencies among variables, detect changes that matter for achieving goals, represent them efficiently to  ...  In response, they should apply both learned patterns for quick response (analogous to fast, intuitive "System 1" decision-making in human psychology) and also slower causal inference and simulation, decision  ...  Bayesian inference of unobserved quantities from observed (or assumed) ones can proceed in any direction.  ... 
doi:10.3390/e23050601 pmid:34068183 fatcat:354opczpwba33l2xoaqu6thr34

Bayesian Networks Modeling for Crop Diseases [chapter]

Chunguang Bi, Guifen Chen
2011 IFIP Advances in Information and Communication Technology  
In the presence of risk and uncertainty, this paper focuses on finding out the best pest control decisionmaking program which is based on the Bayesian network.  ...  The paper describes the flowchart of a Bayesian network and the principles used to calculate the conditional probabilities required in it.  ...  conditional dependences via a directed acyclic graph (DAG).  ... 
doi:10.1007/978-3-642-18333-1_37 fatcat:p7l2b7fcffbuxkeultuvruiami

Data-Driven Synthesis of Full Probabilistic Programs [chapter]

Sarah Chasins, Phitchaya Mangpo Phothilimthana
2017 Lecture Notes in Computer Science  
Probabilistic programming languages (PPLs) provide users a clean syntax for concisely representing probabilistic processes and easy access to sophisticated built-in inference algorithms.  ...  We introduce a data-guided approach to the program mutation stage of simulated annealing; this innovation allows our tool to scale to synthesizing complete probabilistic programs from scratch.  ...  Department of Energy, Office of Science, Office of Basic Energy Sciences Energy Frontier Research Centers program under Award Number FOA-0000619, and grants from DARPA FA8750-14-C-0011 and DARPA FA8750  ... 
doi:10.1007/978-3-319-63387-9_14 fatcat:idnaf7svwrc2thcmndstn6gjxm

Bayesian Analogical Cybernetics [article]

Adam Safron
2019 arXiv   pre-print
From the Bayesian perspective of the Free Energy Principle and Active Inference framework, thought is constituted by dynamics of cascading belief propagation through the nodes of probabilistic generative  ...  It has been argued that all of cognition can be understood in terms of Bayesian inference. It has also been argued that analogy is the core of cognition.  ...  -Thomas Bayes Simply, Bayesian cognitive science is an approach to understanding mental processes in terms of probabilistic inference.  ... 
arXiv:1911.02362v2 fatcat:f7hmtrggvbggvh4izlfgomh4ae

Venture: a higher-order probabilistic programming platform with programmable inference [article]

Vikash Mansinghka and Daniel Selsam and Yura Perov
2014 arXiv   pre-print
Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp.  ...  Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs.  ...  In Venture, the same inference machinery used for state estimation or causal inference can be brought to bear on problems of probabilistic program synthesis.  ... 
arXiv:1404.0099v1 fatcat:e4w6nhrldvgijoxep6i4kedzle

Building large-scale Bayesian networks

2000 Knowledge engineering review (Print)  
Bayesian networks (BNs) model problems that involve uncertainty.  ...  A BN is a directed graph, whose nodes are the uncertain variables and whose edges are the causal or in¯uential links between the variables.  ...  Bayesian networks (also known as Bayesian belief networks, causal probabilistic networks, causal nets, graphical probability networks, probabilistic cause±eect models and probabilistic in¯uence diagrams  ... 
doi:10.1017/s0269888900003039 fatcat:a2cv2za57nbatatlrw3obxsy6a

The role of causality in judgment under uncertainty

Tevye R. Krynski, Joshua B. Tenenbaum
2007 Journal of experimental psychology. General  
variables via Bayesian inference over the model.  ...  (c) Judgments are made via Bayesian inference over the parameterized causal model.  ... 
doi:10.1037/0096-3445.136.3.430 pmid:17696692 fatcat:65xpw5a6zbhltl4wh3in4iqbge

An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation

Adam Safron
2020 Frontiers in Artificial Intelligence  
In FEP-AI, minds and brains are predictive controllers for autonomous systems, where action-driven perception is realized as probabilistic inference.  ...  resolved by integrating IIT with FEP-AI, where integrated information only entails consciousness for systems with perspectival reference frames capable of generating models with spatial, temporal, and causal  ...  ., free energy) via Bayesian model selection in accordance with FEP-AI.  ... 
doi:10.3389/frai.2020.00030 pmid:33733149 pmcid:PMC7861340 doaj:8441e3d6220f4f948b123f02fac73f26 fatcat:pniqrbgm4zaujmsmqwp77pfm2a
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