Search results

...ernoulli distribution|Bernoulli]] model (TI-HBM)''' is an alternative to [[hidden Markov model]] (HMM) for [[automatic speech recognition]]. Contrary to HMM, ...rkov: Investigation of state transition regime in switching-state acoustic models," ''Signal Processing'', vol. 89, no. 4, pp. 662–668, April 2009. ...

...n Haykin]]. {{ISBN|0-471-22154-6}}</ref> but related switching state-space models have been in use. ...]], [[Geoffrey E. Hinton]]. Variational Learning for Switching State-Space Models. Neural Computation, 12(4):963–996.</ref> ...

...permutations]]'' of a sentence.<ref name=":0">{{Cite web |title=Pretrained models — transformers 2.0.0 documentation |url=https://huggingface.co/transformers Like the causal masking for GPT models, this two-stream masked architecture allows the model to train on all token ...

...systems analysis. Generalized filtering furnishes posterior densities over hidden states (and parameters) generating observed data using a generalized [[grad * ''Hidden states'' <math>X:X \times U \times \Omega \to \mathbb{R}</math> – that cau ...

...t4=Strickert|first4=Marc|year=2004|title=Recursive self-organizing network models|journal=Neural Networks|volume=17|issue=8–9|pages=1061–1085|doi=10.1016/j.n ...=Y. Ng|first6=Andrew|last7=Potts|first7=Christopher|chapter=Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank|title=EMNLP 2013|ch ...

...iagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units)]] ...model''') is a [[generative model|generative]] [[stochastic neural network|stochastic]] [[artificial neural network]] that can learn a [[probability distribution ...

...s, MSM compares favorably with standard [[Stochastic volatility|volatility models]] such as [[GARCH|GARCH(1,1)]] and FIGARCH both in- and out-of-sample. MSM ...ten provides better volatility forecasts than some of the best traditional models both in and out of sample. Calvet and Fisher<ref name=CF2004/> report consi ...

| title = Hierarchical Bayesian Nonparametric Models with Applications ...name="teh2006" /> as a formalization and generalization of the [[infinite hidden Markov model]] published in 2002.<ref name="beal2002"/> ...

{{further|Panel analysis#Dynamic panel models|Arellano–Bond estimator}} ...er to: {{cite journal |first=Takeshi |last=Amemiya |year=1984 |title=Tobit models: A survey |journal=Journal of Econometrics |volume=24 |issue=1–2 |pages=3–6 ...

{{See also|Graphical models}} ...or both <math>\textstyle X</math> and <math>\textstyle Y</math>. Sometimes models are intimately associated with a particular learning rule. A common use of ...

...l |doi=10.1016/0304-4076(75)90032-9 |title=Maximum score estimation of the stochastic utility model of choice |date=1975 |last1=Manski |first1=Charles F. |journa One way to model this behavior is called '''stochastic rationality'''. It is assumed that each agent has an unobserved ''state'', ...

...d''' is a numerical method that combines [[deep learning]] with [[Backward stochastic differential equation]] (BSDE). This method is particularly useful for solv ===Backwards stochastic differential equations=== ...

...M. J., Bates, M., and Zhuang, X. (2006). Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nature methods, 3(10), 793–796.< ...604-4|access-date=2021-01-30}}</ref> The [[Langevin equation]] describes a stochastic particle driven by a Brownian force <math>\Xi</math> and a field of force ( ...

...|first=Charles F. |date=1975-08-01 |title=Maximum score estimation of the stochastic utility model of choice |url=https://dx.doi.org/10.1016/0304-4076%2875%2990 One way to model this behavior is called '''stochastic rationality'''. It is assumed that each agent has an unobserved ''state'', ...

...[[image recognition]] and [[natural language processing]]. However, these models are often over-parameterized, containing far more weights and connections t ...the weights are randomly initialized. When this network is optimized using stochastic gradient descent (SGD), it reaches a minimum validation loss <math>l</math> ...

In most applications, the signal is [[Stochastic process|stochastic]]; nevertheless, it can have [[Deterministic system|deterministic]] ON-OFF ...dimension.<ref>{{cite book | author= Erhan Cinlar | title=Introduction to Stochastic Processes | publisher=Prentice Hall Inc, New Jersey |year=1975 |isbn=978-0- ...

...redict [[protein-protein interactions]] (PPI). It is also used to identify hidden groups of terrorists and criminals in security related applications.<ref na In statistics, generative random graph models such as [[stochastic block model]]s propose an approach to generate links between nodes in a [[r ...

...gradient ascent]]. Since the key part of any policy gradient method is the stochastic estimation of the policy gradient, they are also studied under the title of {{hidden begin|style=width:100%|ta1=center|border=1px #aaa solid|title=Proof}} ...

...ce these unwanted shifts to speed up training and to produce more reliable models. ...onducted over the entire training set, but to use this step jointly with [[stochastic optimization]] methods, it is impractical to use the global information. Th ...

mixture models.<ref name="Fruhwirth-Schnatter2006" /> The Bayesian approach also allows fo ...erical, with identical eigenvalues (I). This yields 14 possible clustering models, shown in this table: ...