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Found 7 papers, 4 papers with code
Sequential Monte Carlo Learning for Time Series Structure Discovery
This paper presents a new approach to automatically discovering accurate models of complex time series data.
Estimators of Entropy and Information via Inference in Probabilistic Models
Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimensions.
Hierarchical Infinite Relational Model
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data.
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries.
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
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 programs in these modeling languages given observed data.
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions
Unlike most existing test statistics, the proposed test statistic is distribution-free and its exact (non-asymptotic) sampling distribution is known in closed form.
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series
We apply the technique to challenging forecasting and imputation tasks using seasonal flu data from the US Center for Disease Control and Prevention, demonstrating superior forecasting accuracy and competitive imputation accuracy as compared to multiple widely used baselines.
