“An Overview of Probabilistic Programming” by Vikash K. Mansinghka (2015)
Points: 2 | Comments: 0 | Author: bacr
Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabil…
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approaches to probabilistic deep lear...
Probabilistic exact deletion and probabilistic no-signalling
In this work we show that one cannot use non-local resources for probabilistic signalling even if one can delete a quantum state with the help of probabilistic quantum deletion machine. Here we find that probabilistic quantum deletion machine is no...
What is the difference between probabilistic programming vs. probabilistic machi…
Tags: machine-learning, bayesian, probabilistic-programming | Score: 7
Approximately Dual and Pseudo-Dual Probabilistic Frames
This paper studies properties of dual probabilistic frames -- in particular in relation to redundancy -- and introduces both approximately dual probabilistic frames and pseudo-dual probabilistic frames. We show that the canonical dual probabilistic f...
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog...
Bit Blasting Probabilistic Programs
Probabilistic programming languages (PPLs) are expressive means for creating and reasoning about probabilistic models. Unfortunately hybrid probabilistic programs, involving both continuous and discrete structures, are not well supported by today's P...
jenspetersen/probabilistic-unet
A PyTorch implementation of the Probabilistic U-Net, applied to probabilistic glioma growth (⭐ 43)
Inference and learning in probabilistic logic programs using weighted Boolean fo…
Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic...
Is there any difference between Random and Probabilistic?
Mar 26, 2015 · It seems i can't directly say probabilistic and random are identical . But this is telling : random experiment is a probabilistic experiment. Is there any difference between Random and …
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make in...
What is the difference between the probabilistic and non-probabilistic ...
A probabilistic approach (such as Random Forest) would yield a probability distribution over a set of classes for each input sample. A deterministic approach (such as SVM) does not model the …
Is there any difference between Random and Probabilistic?
Mar 26, 2015 · It seems i can't directly say probabilistic and random are identical . But this is telling : random experiment is a probabilistic experiment. Is there any difference between Random and …
Equivalence and Similarity Refutation for Probabilistic Programs
We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two fundamental relational properties of probabilistic programs that are es...
On the Hardness of Approximating Distributions with Tractable Probabilistic Mode…
A fundamental challenge in probabilistic modeling is to balance expressivity and inference efficiency. Tractable probabilistic models (TPMs) aim to directly address this tradeoff by imposing constraints that guarantee efficient inference of certain q...
A Probabilistic Shaping Approach for Optical Region-of-Interest Signaling
We propose a probabilistic shaping approach for region-of-interest signaling, where a low-rate signal controls the desired probabilistic ranges of a high-rate data stream using a flexible distribution controller. In addition, we introduce run-length-...
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precise way in which the probabilistic program is written. A standard way of...
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
Neural probabilistic logic systems follow the neuro-symbolic (NeSy) paradigm by combining the perceptive and learning capabilities of neural networks with the robustness of probabilistic logic. Learning corresponds to likelihood optimization of the n...
A hybrid model of kernel density estimation and quantile regression for GEFCom20…
We present a model for generating probabilistic forecasts by combining kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. The KDE...
Infinite Probabilistic Databases
Probabilistic databases (PDBs) model uncertainty in data in a quantitative way. In the established formal framework, probabilistic (relational) databases are finite probability spaces over relational database instances. This finiteness can clash with...