statistics and econometrics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR) model. BVAR differs with...

Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. VAR is a type...

wikidata descriptions as a fallback Bayesian vector autoregression – use of Bayesian methods to estimate a vector autoregression modelPages displaying wikidata...

Bayesian inference (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to calculate a probability...

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...

Bayesian search theory Bayesian spam filtering Bayesian statistics Bayesian tool for methylation analysis Bayesian vector autoregression BCMP network – queueing...

model is a special case of the vector autoregressive model, the computation of the impulse response in vector autoregression#impulse response applies here...

of the error term. Bayesian linear regression applies the framework of Bayesian statistics to linear regression. (See also Bayesian multivariate linear...

In statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among...

{\displaystyle {\boldsymbol {q_{1}}}} is a vector, and q 0 {\displaystyle q_{0}} is a scalar), which is relevant for Bayesian classification/decision theory using...

models can improve accuracy. Such models can be built using bayesian vector autoregressions, dynamic factors, bridge equations using time series methods...

Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is...

computations were developed, approximations for Bayesian clustering rules were devised. Some Bayesian procedures involve the calculation of group-membership...

space are a sequence of p {\displaystyle p} unit vectors, where the i {\displaystyle i} -th vector is the direction of a line that best fits the data...

maximum) gives an indication of the estimate's precision. In contrast, in Bayesian statistics, the estimate of interest is the converse of the likelihood...

expectations. A similar test involving more variables can be applied with vector autoregression. The validity of the Granger causality test has been challenged...

equation, with different dependent variables, estimated together. Vector autoregression involves simultaneous regressions of various time series variables...

; Yang, Y. (June 2018). "Bridging AIC and BIC: A New Criterion for Autoregression". IEEE Transactions on Information Theory. 64 (6): 4024–4043. arXiv:1508...

356–366. doi:10.1257/aer.90.3.356. JSTOR 117333. ——— (1997). "Bayesian Vector Autoregressions with Stochastic Volatility". Econometrica. 65 (1): 59–73. CiteSeerX 10...

(Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR) Frequency domain Spectral density estimation Fourier analysis...

The use of a Bayesian design does not force statisticians to use Bayesian methods to analyze the data, however. Indeed, the "Bayesian" label for probability-based...

want to make inferences is y ∈ Y {\displaystyle y\in Y} , where the random vector Y {\displaystyle Y} is a function of an unknown parameter, θ {\displaystyle...

method on many statistical computing packages. Other approaches, including Bayesian regression and least squares fitting to variance stabilized responses,...

minimization problem). In a Bayesian context, this is equivalent to placing a zero-mean normally distributed prior on the parameter vector. An alternative regularized...

been executed using Bayesian methods, mixed linear models and meta-regression approaches.[citation needed] Specifying a Bayesian network meta-analysis...

interval estimation (including Fisher's fiducial intervals and objective Bayesian intervals). Robinson called this example "[p]ossibly the best known counterexample...

and other Bayes methods. Connections have been made between the FDR and Bayesian approaches (including empirical Bayes methods), thresholding wavelets coefficients...

information becomes available. Bayesian inference is playing an increasingly important role in geostatistics. Bayesian estimation implements kriging through...

An estimation procedure that is often claimed to be part of Bayesian statistics is the maximum a posteriori (MAP) estimate of an unknown quantity, that...

inference need have a Bayesian interpretation. Analyses which are not formally Bayesian can be (logically) incoherent; a feature of Bayesian procedures which...