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06/13/2023
Differentiating Metropolis-Hastings to Optimize Intractable Densities
We develop an algorithm for automatic differentiation of Metropolis-Hast...
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03/14/2023
Methods and applications of PDMP samplers with boundary conditions
We extend Monte Carlo samplers based on piecewise deterministic Markov p...
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10/16/2022
Automatic Differentiation of Programs with Discrete Randomness
Automatic differentiation (AD), a technique for constructing new program...
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05/26/2022
Flexible Group Fairness Metrics for Survival Analysis
Algorithmic fairness is an increasingly important field concerned with d...
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11/22/2021
Conditioning continuous-time Markov processes by guiding
A continuous-time Markov process X can be conditioned to be in a given s...
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10/01/2021
Applied Measure Theory for Probabilistic Modeling
Probabilistic programming and statistical computing are vibrant areas in...
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03/15/2021
Sticky PDMP samplers for sparse and local inference problems
We construct a new class of efficient Monte Carlo methods based on conti...
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11/16/2020
Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations
We study a nonparametric Bayesian approach to estimation of the volatili...
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10/07/2020
Automatic Backward Filtering Forward Guiding for Markov processes and graphical models
We incorporate discrete and continuous time Markov processes as building...
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02/03/2020
Diffusion bridges for stochastic Hamiltonian systems with applications to shape analysis
Stochastically evolving geometric systems are studied in geometric mecha...
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01/16/2020
A piecewise deterministic Monte Carlo method for diffusion bridges
We introduce the use of the Zig-Zag sampler to the problem of sampling o...
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10/17/2018
Bayesian wavelet de-noising with the caravan prior
According to both domain expertise knowledge and empirical evidence, wav...
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10/03/2018
Simulation of elliptic and hypo-elliptic conditional diffusions
Suppose X is a multidimensional diffusion process. Assume that at time z...
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05/15/2018
Nonparametric Bayesian volatility learning under microstructure noise
Aiming at financial applications, we study the problem of learning the v...
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04/30/2018
Nonparametric Bayesian inference for Lévy subordinators
Given discrete time observations over a growing time interval, we consid...
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04/10/2018
Fast and scalable non-parametric Bayesian inference for Poisson point processes
We study the problem of non-parametric Bayesian estimation of the intens...
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01/30/2018
Nonparametric Bayesian volatility estimation
Given discrete time observations over a fixed time interval, we study a ...
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12/11/2017
Continuous-discrete smoothing of diffusions
Suppose X is a multivariate diffusion process that is observed discretel...
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08/16/2017
