The detection of change points is a pivotal task in statistical analysis. In the quantum realm, it is a new primitive where one aims at identifying the point where a source that supposedly prepares a sequence of particles in identical quantum states starts preparing a mutated one. We obtain the optimal procedure to identify the change point with certainty-naturally at the price of having a cert...

We consider the problem of Bayesian inference for continuous-time multi-stable stochastic systems which can change both their diffusion and drift parameters at discrete times. We propose exact inference and sampling methodologies for two specific cases where the discontinuous dynamics is given by a Poisson process and a two-state Markovian switch. We test the methodology on simulated data, and ...

Change-point detection has been a fundamental problem in quality control, hazards monitoring and cybersecurity. With the ever-growing complexity of system and enlarging number of sensors, multi-sensor detection algorithms are in real demand. However, most existing detection algorithms consider a centralized approach, meaning that one collects all data at one location, which sometimes may be inf...

ruptures is a Python library for o ine change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular struct...

Change-point hazard rate models arise, for example, in applying \burn-in" techniques to screen defective items and in studying times until undesirable side eeects occur in clinical trials. The classical approach develops estimates of model parameters, with particular interest in the threshold or change-point parameter, but exclusively in terms of asymptotic properties. Such asymptotics can be p...

A new method is required for change-point testing of precipitation data that is capable of applying valid precipitation models. First, stochastic precipation models are researched and classified. Typically, the occurrence of rain is modeled using a two-state, first-order Markov chain, and the intensity of rain is modeled using a two-parameter gamma distribution. Using the likelihood ratio test ...

Change-point problems arise when di/erent subsequences of a data series follow di/erent statistical distributions – commonly of the same functional form but having di/erent parameters. This paper develops an exact approach for 3nding maximum likelihood estimates of the change points and within-segment parameters when the functional form is within the general exponential family. The algorithm, a...

We develop a method of estimating change{points of a function in the case of indirect noisy observations. As two paradigmatic problems we consider de-convolution and errors-in-variables regression. We estimate the scalar products of our indirectly observed function with appropriate test functions, which are shifted over the interval of interest. An estimator of the change point is obtained by t...

In this work, methods to detect one or several change points in multivariate time series are reviewed. They include retrospective (o -line) procedure such as maximum likelihood estimation, regression, kernel methods, etc. In this large area of research, applications are numerous and diverse; many di erent models and operational constraints (on precision, complexity,...) exist. A formal framewor...

Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for...