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Found 13 papers, 3 papers with code
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
Bayesian Inference of Spreading Processes on Networks
Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network.
The Social Bow Tie
We also investigate what aspects of the bow tie are most predictive of tie strength in two distinct social networks: a collection of 75 rural villages in India and a nationwide call network of European mobile phone users.
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science.
Physics and Society Social and Information Networks
A systematic review of smartphone-based human activity recognition for health research
We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition.
Communities in Networks
We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science.
Physics and Society Statistical Mechanics Computers and Society Discrete Mathematics Statistics Theory Adaptation and Self-Organizing Systems Computational Physics Statistics Theory
Cost-based feature selection for network model choice
This approach reduced the computational cost by a factor of 50 without affecting classification accuracy.
Minimising the Expected Posterior Entropy Yields Optimal Summary Statistics
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference.
Nonparametric Additive Value Functions: Interpretable Reinforcement Learning with an Application to Surgical Recovery
Under this approach, we are able to locally approximate the action-value function and retrieve the nonlinear, independent contribution of select features as well as joint feature pairs.
Flexible Bayesian Inference on Partially Observed Epidemics
In this paper, we consider Bayesian inference on the spreading parameters of an SIR contagion on a known, static network, where information regarding individual disease status is known only from a series of tests (positive or negative disease status).
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique.
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
The objectives of this paper are: (i) To propose a general predictive framework for regression and classification using neural network (NN) modeling, which incorporates survey weights into the estimation process; (ii) To introduce an uncertainty quantification algorithm for model prediction, tailored for data from complex survey designs; (iii) To apply this method in developing robust risk score models to assess the risk of Diabetes Mellitus in the US population, utilizing data from the NHANES 2011-2014 cohort.
Accounting for contact network uncertainty in epidemic inferences
However, in realistic settings, the observed data often serves as an imperfect proxy of the actual contact patterns in the population.
