24 papers · ranked by Valyu relevance
Edoardo M Airoldi, William Noble
A probabilistic graphical model defines a family of probability distributions that can be represented in terms of a graph. Nodes in the graph correspond to random variables; its structure translates into statistical dependencies (among such variables) that drive the computation of joint, conditional, and marginal…
Guillaume Baudart, Martin Hirzel, Louis Mandel
Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages. If successful, this would be a big step forward in machine learning and programming languages. Unfortunately, as of now, this new crop of languages is hard to use and understand.…
Kristijan Armeni, Roel M. Willems, Stefan Frank
Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a…
Zoubin Ghahramani
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous…
Ruggero Basanisi, Andrea Brovelli, Emilio Cartoni, Gianluca Baldassarre
In mammals, goal-directed and planning processes support flexible behaviour usable to face new situations or changed conditions that cannot be tackled through more efficient but rigid habitual behaviours. Within the Bayesian modelling approach of brain and behaviour, probabilistic models have been proposed to perform…
Jaron Thompson, Nicholas Lubbers, Marie E. Kroeger, Rae DeVan + 3 more
The overwhelming complexity of microbiomes makes it difficult to decipher functional relationships between specific microbes and ecosystem properties. While machine learning analyses have demonstrated an impressive ability to correlate microbial community composition with macroscopic functions, mechanisms that dictate…
Lawrence M. Murray, Thomas B. Schön
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this…
Vaishak Belle
Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent. Probabilistic programming has rapidly emerged as a key paradigm to integrate probabilistic concepts with…
Julian Rode, Torsten Goerke, Lutz Brusch, Fabian Rost
Correct estimates of cell proliferation rates are crucial for quantitative models of the development, maintenance and regeneration of tissues. Continuous labeling assays are used to infer proliferation rates in vivo. So far, the experimental and theoretical study of continuous labeling assays focused on the dynamics of…
Hexuan Liu, Jimin Kim, Eli Shlizerman
We propose a data-driven approach to represent neuronal network dynamics as a Probabilistic Graphical Model (PGM). Our approach learns the PGM structure by employing dimension reduction to network response dynamics evoked by stimuli applied to each neuron separately. The outcome model captures how stimuli propagate…
Luc De Raedt, Angelika Kimmig
A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax…
Chenxi Wang, Jihui Zhao, Jingjing Zheng, Barak Raveh + 2 more
Developing and optimizing models for complex systems poses challenges due to the inherent complexity introduced by multiple types of input information and sources of uncertainty. In this study, we utilize Bayesian formalism to analytically examine the propagation of probability in the modeling process and propose…
Oleg Kiselyov, Chung-chieh Shan
Probabilistic programming languages and modeling toolkits are two modular ways to build and reuse stochastic models and inference procedures. Combining strengths of both, we express models and inference as generalized coroutines in the same general-purpose language. We use existing facilities of the language, such as…
Roland R. Ramsahai
Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in…
Ruefei He, Jonathan Shapiro
In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
Authors not listed
Finite-temperature lattice free energy differences between polymorphs of molecular crystals are fundamental to understanding and predicting the relative stability relationships underpinning polymorphism, yet are computationally expensive to obtain. Here, we implement and critically assess machine-learning-enabled…
R. Olson, S.-I. An, Y. Fan, W. Chang + 2 more
A major conundrum in climate science is how to account for dependence between climate models. This complicates interpretation of probabilistic projections derived from such models. Here we show that this problem can be addressed using a novel method to test multiple non-exclusive hypotheses, and to make predictions…
Vinicius Santana, Erbet Costa, Carine Rebello, Ana Mafalda Ribeiro + 2 more
This study presents an innovative framework for classifying and predicting odor intensity in perfumery, combining scientific machine learning with mechanistic modeling to enhance fragrance design precision. A probabilistic weight assignment is introduced, utilizing scent classifier outputs to determine the contribution…
Quentin Ruyant, Mauricio Suárez
Modal Empiricism in philosophy of science proposes to understand the possibility of modal knowledge from experience by replacing talk of possible worlds with talk of possible situations, which are coarse-grained, bounded and relative to background conditions. This allows for an induction towards objective necessity…
Olga Egorova, Roohollah Hafizi, David C. Woods, Graeme Day
The prediction of crystal structures from first principles requires highly accurate energies for large numbers of putative crystal structures. The accuracy of solid state density functional theory (DFT) calculations is often required, but hundreds or more structures can be present in the low energy region of interest…
Ang Li, Luis Pericchi, Kun Wang
There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using probit regression and logistic regression with normal priors. In this…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…