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Markov chain Monte Carlo (MCMC) simulations are modeled as driven by true random numbers. We consider variance bounding Markov chains driven by a deterministic sequence of numbers. The star-discrepancy provides a measure of efficiency of…

Computation · Statistics 2014-12-03 Josef Dick , Daniel Rudolf

A methodology is proposed for the calculation of the truncation error of finite volume discretisations of the incompressible Navier-Stokes equations on colocated grids. The truncation error is estimated by restricting the solution obtained…

Computational Physics · Physics 2015-08-13 Alexandros Syrakos , Apostolos Goulas

Approximate Bayesian computation has emerged as a standard computational tool when dealing with the increasingly common scenario of completely intractable likelihood functions in Bayesian inference. We show that many common Markov chain…

Methodology · Statistics 2014-08-12 Anthony Lee , Krzysztof Latuszynski

In this paper, network error control coding is studied for robust and efficient multicast in a directed acyclic network with imperfect links. The block network error control coding framework, BNEC, is presented and the capability of the…

Information Theory · Computer Science 2008-09-25 Hossein Bahramgiri , Farshad Lahouti

Adaptive and interacting Markov Chains Monte Carlo (MCMC) algorithms are a novel class of non-Markovian algorithms aimed at improving the simulation efficiency for complicated target distributions. In this paper, we study a general…

Statistics Theory · Mathematics 2011-07-15 Gersende Fort , Eric Moulines , Pierre Priouret , Pierre Vandekerkhove

We prove that the blocklength $n$ of a linear $3$-query locally correctable code (LCC) $\mathcal{L} \colon {\mathbb F}^k \to {\mathbb F}^n$ with distance $\delta$ must be at least $n \geq 2^{\Omega\left(\left(\frac{\delta^2 k}{(|{\mathbb…

Computational Complexity · Computer Science 2023-11-02 Pravesh K. Kothari , Peter Manohar

We derive novel upper and lower finite-length bounds of the error probability in joint source-channel coding when the source obeys an ergodic Markov process and the channel is a Markovian additive channel or a Markovian conditional additive…

Information Theory · Computer Science 2017-05-03 Ryo Yaguchi , Masahito Hayashi

In this article we investigate model order reduction of large-scale systems using time-limited balanced truncation, which restricts the well known balanced truncation framework to prescribed finite time intervals. The main emphasis is on…

Numerical Analysis · Mathematics 2018-01-08 Patrick Kürschner

Markov chain Monte Carlo (MCMC) methods generate samples that are asymptotically distributed from a target distribution of interest as the number of iterations goes to infinity. Various theoretical results provide upper bounds on the…

Computation · Statistics 2019-10-30 Niloy Biswas , Pierre E. Jacob , Paul Vanetti

This paper considers contextual bandits with a finite number of arms, where the contexts are independent and identically distributed $d$-dimensional random vectors, and the expected rewards are linear in both the arm parameters and…

Machine Learning · Statistics 2025-05-07 Yanglei Song , Meng zhou

We present a methodology for bounding the error term of an asymptotic solution to a singularly perturbed optimal control (SPOC) problem whose exact solution is known to be computationally intractable. In previous works, reduced or…

Optimization and Control · Mathematics 2016-10-20 Sei Howe , Panos Parpas

This paper applies error-exponent and dispersion-style analyses to derive finite-blocklength achievability bounds for low-density parity-check (LDPC) codes over the point-to-point channel (PPC) and multiple access channel (MAC). The…

Information Theory · Computer Science 2020-05-14 Yuxin Liu , Michelle Effros

Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions. Markov…

Computation · Statistics 2020-06-17 Lawrence Middleton , George Deligiannidis , Arnaud Doucet , Pierre E. Jacob

The block coordinate descent (BCD) method is widely used for minimizing a continuous function f of several block variables. At each iteration of this method, a single block of variables is optimized, while the remaining variables are held…

Optimization and Control · Mathematics 2012-09-12 Meisam Razaviyayn , Mingyi Hong , Zhi-Quan Luo

The distributed biased min-consensus (DBMC) protocol is an iterative scheme that solves the shortest path problem asymptotically, requiring only local information exchange between neighboring nodes. By appropriately designing the gain…

Systems and Control · Electrical Eng. & Systems 2025-09-25 Zicheng Huang , Wangzhi Zhou , Yuanqiu Mo

The exponential server timing channel is known to be the simplest, and in some sense canonical, queuing timing channel. The capacity of this infinite-memory channel is known. Here, we discuss practical finite-length restrictions on the…

Information Theory · Computer Science 2016-05-26 Thomas J. Riedl , Todd P. Coleman , Andrew C. Singer

A bound on the error introduced by truncating a quantum addition is given. This bound shows that only a few controlled rotation gates will be necessary to get a reliable computation.

Quantum Physics · Physics 2007-05-23 Nathan W. Panike

Quantum many-body systems involving bosonic modes or gauge fields have infinite-dimensional local Hilbert spaces which must be truncated to perform simulations of real-time dynamics on classical or quantum computers. To analyze the…

Quantum Physics · Physics 2022-09-28 Yu Tong , Victor V. Albert , Jarrod R. McClean , John Preskill , Yuan Su

Assuming iterative decoding for binary erasure channels (BECs), a novel tree-based technique for upper bounding the bit error rates (BERs) of arbitrary, finite low-density parity-check (LDPC) codes is provided and the resulting bound can be…

Information Theory · Computer Science 2007-07-13 Chih-Chun Wang , Sanjeev R. Kulkarni , H. Vincent Poor

We study upper and lower bounds on the sample-complexity of learning near-optimal behaviour in finite-state discounted Markov Decision Processes (MDPs). For the upper bound we make the assumption that each action leads to at most two…

Machine Learning · Computer Science 2013-05-17 Tor Lattimore , Marcus Hutter