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**Time-series segmentation** is a method of time-series analysis in which an input time-series is divided into a sequence of discrete segments in order to reveal the underlying properties of its source. A typical application of time-series segmentation is in speaker diarization, in which an audio signal is partitioned into several pieces according to who is speaking at what times. Algorithms based on change-point detection include sliding windows, bottom-up, and top-down methods.^{ [1] } Probabilistic methods based on hidden Markov models have also proved useful in solving this problem.^{ [2] }

**Speaker diarisation** is the process of partitioning an input audio stream into homogeneous segments according to the speaker identity. It can enhance the readability of an automatic speech transcription by structuring the audio stream into speaker turns and, when used together with speaker recognition systems, by providing the speaker’s true identity. It is used to answer the question "who spoke when?" Speaker diarisation is a combination of speaker segmentation and speaker clustering. The first aims at finding speaker change points in an audio stream. The second aims at grouping together speech segments on the basis of speaker characteristics.

In statistical analysis, **change detection** or **change point detection** tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes.

**Hidden Markov Model** (**HMM**) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved states.

It is often the case that a time-series can be represented as a sequence of discrete segments of finite length. For example, the trajectory of a stock market could be partitioned into regions that lie in between important world events, the input to a handwriting recognition application could be segmented into the various words or letters that it was believed to consist of, or the audio recording of a conference could be divided according to who was speaking when. In the latter two cases, one may take advantage of the fact that the label assignments of individual segments may repeat themselves (for example, if a person speaks at several separate occasions during a conference) by attempting to cluster the segments according to their distinguishing properties (such as the spectral content of each speaker's voice). There are two general approaches to this problem. The first involves looking for change points in the time-series: for example, one may assign a segment boundary whenever there is a large jump in the average value of the signal. The second approach involves assuming that each segment in the time-series is generated by a system with distinct parameters, and then inferring the most probable segment locations and the system parameters that describe them. While the first approach tends to only look for changes in a short window of time, the second approach generally takes into account the entire time-series when deciding which label to assign to a given point.

A **time series** is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

A **stock market**, **equity market** or **share market** is the aggregation of buyers and sellers of stocks, which represent ownership claims on businesses; these may include *securities* listed on a public stock exchange, as well as stock that is only traded privately. Examples of the latter include shares of private companies which are sold to investors through equity crowdfunding platforms. Stock exchanges list shares of common equity as well as other security types, e.g. corporate bonds and convertible bonds.

**Handwriting recognition** (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices. The image of the written text may be sensed "off line" from a piece of paper by optical scanning or intelligent word recognition. Alternatively, the movements of the pen tip may be sensed "on line", for example by a pen-based computer screen surface, a generally easier task as there are more clues available.

Under the hidden Markov model, the time-series is assumed to have been generated as the system transitions among a set of discrete, hidden states . At each time , a sample is drawn from an observation (or emission) distribution indexed by the current hidden state, i.e., . The goal of the segmentation problem is to infer the hidden state at each time, as well as the parameters describing the emission distribution associated with each hidden state. Hidden state sequence and emission distribution parameters can be learned using the Baum-Welch algorithm, which is a variant of expectation maximization applied to HMMs. Typically in the segmentation problem self-transition probabilities among states are assumed to be high, such that the system remains in each state for nonnegligible time. More robust parameter-learning methods involve placing hierarchical Dirichlet process priors over the HMM transition matrix.^{ [3] }

In statistics and machine learning, the **hierarchical Dirichlet process** (**HDP**) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process. This method allows groups to share statistical strength via sharing of clusters across groups. The base distribution being drawn from a Dirichlet process is important, because draws from a Dirichlet process are atomic probability measures, and the atoms will appear in all group-level Dirichlet processes. Since each atom corresponds to a cluster, clusters are shared across all groups. It was developed by Yee Whye Teh, Michael I. Jordan, Matthew J. Beal and David Blei and published in the *Journal of the American Statistical Association* in 2006, as a formalization and generalization of the infinite hidden Markov model published in 2002.

A **Markov chain** is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.

The method of **least squares** is a standard approach in regression analysis to approximate the solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. "Least squares" means that the overall solution minimizes the sum of the squares of the residuals made in the results of every single equation.

**Pattern recognition** is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases (KDD), and is often used interchangeably with these terms. However, these are distinguished: machine learning is one approach to pattern recognition, while other approaches include hand-crafted rules or heuristics; and pattern recognition is one approach to artificial intelligence, while other approaches include symbolic artificial intelligence. A modern definition of pattern recognition is:

The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.

In statistics, an **expectation–maximization** (**EM**) **algorithm** is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the *E* step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

In statistics, **Gibbs sampling** or a **Gibbs sampler** is a Markov chain Monte Carlo (MCMC) algorithm for obtaining a sequence of observations which are approximated from a specified multivariate probability distribution, when direct sampling is difficult. This sequence can be used to approximate the joint distribution ; to approximate the marginal distribution of one of the variables, or some subset of the variables ; or to compute an integral. Typically, some of the variables correspond to observations whose values are known, and hence do not need to be sampled.

In electrical engineering, computer science, statistical computing and bioinformatics, the **Baum–Welch algorithm** is used to find the unknown parameters of a hidden Markov model (HMM). It makes use of a forward-backward algorithm.

In statistics, a **mixture model** is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information.

The **forward algorithm**, in the context of a hidden Markov model (HMM), is used to calculate a 'belief state': the probability of a state at a certain time, given the history of evidence. The process is also known as *filtering*. The forward algorithm is closely related to, but distinct from, the Viterbi algorithm.

In probability and statistics, the **Dirichlet distribution**, often denoted , is a family of continuous multivariate probability distributions parameterized by a vector of positive reals. It is a multivariate generalization of the beta distribution, hence its alternative name of **Multivariate Beta distribution (MBD)**. Dirichlet distributions are commonly used as prior distributions in Bayesian statistics, and in fact the Dirichlet distribution is the conjugate prior of the categorical distribution and multinomial distribution.

**Conditional random fields** (**CRFs**) are a class of statistical modeling method often applied in pattern recognition and machine learning and used for structured prediction. CRFs fall into the sequence modeling family. Whereas a discrete classifier predicts a label for a single sample without considering "neighboring" samples, a CRF can take context into account; e.g., the linear chain CRF predicts sequences of labels for sequences of input samples.

**Uncertainty quantification (UQ)** is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if we exactly knew the speed, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense.

The **hierarchical hidden Markov model (HHMM)** is a statistical model derived from the hidden Markov model (HMM). In an HHMM each state is considered to be a self-contained probabilistic model. More precisely each state of the HHMM is itself an HHMM.

In probability theory and statistics, a **categorical distribution** is a discrete probability distribution that describes the possible results of a random variable that can take on one of *K* possible categories, with the probability of each category separately specified. There is no innate underlying ordering of these outcomes, but numerical labels are often attached for convenience in describing the distribution,. The *K*-dimensional categorical distribution is the most general distribution over a *K*-way event; any other discrete distribution over a size-*K* sample space is a special case. The parameters specifying the probabilities of each possible outcome are constrained only by the fact that each must be in the range 0 to 1, and all must sum to 1.

The **discrete phase-type distribution** is a probability distribution that results from a system of one or more inter-related geometric distributions occurring in sequence, or phases. The sequence in which each of the phases occur may itself be a stochastic process. The distribution can be represented by a random variable describing the time until absorption of an absorbing Markov chain with one absorbing state. Each of the states of the Markov chain represents one of the phases.

**Linear least squares** (**LLS**) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods.

**Structured prediction** or **structured (output) learning** is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than scalar discrete or real values.

In machine learning, a **maximum-entropy Markov model** (**MEMM**), or **conditional Markov model** (**CMM**), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging and information extraction.

In computer vision, **rigid motion segmentation** is the process of separating regions, features, or trajectories from a video sequence into coherent subsets of space and time. These subsets correspond to independent rigidly moving objects in the scene. The goal of this segmentation is to differentiate and extract the meaningful rigid motion from the background and analyze it. Image segmentation techniques labels the pixels to be a part of pixels with certain characteristics at a particular time. Here, the pixels are segmented depending on its relative movement over a period of time i.e. the time of the video sequence.

- ↑ Keogh, Eamonn, et al. "Segmenting time series: A survey and novel approach." Data mining in time series databases 57 (2004): 1-22.
- ↑ Fox, Emily B., et al. "An HDP-HMM for systems with state persistence." Proceedings of the 25th international conference on Machine learning. ACM, 2008.
- ↑ Teh, Yee Whye, et al. "Hierarchical dirichlet processes." Journal of the American Statistical Association 101.476 (2006).

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