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Skutečné metody odpovídající vašemu filtru.
SeřaditPopularitaA–ZZ–ANejnovější
simulation

Bayesian Markov Model

A Bayesian Markov model is a state-transition simulation method that combines Markov chain cohort modeling with Bayesian statistical inference. By placing prior distributions on transition probabilities and updating them with observed data, the approach propagates full parameter uncertainty through the simulation, yiel

2 zdrojů1990
causal inference

Bayesian Matching Estimator

The Bayesian Matching Estimator estimates average treatment effects in observational studies by combining classical nearest-neighbour or kernel matching with a Bayesian posterior over the treatment effect. It inherits matching's covariate-balancing logic while propagating uncertainty through a full posterior distributi

2 zdrojů1978
psychometrics

Bayesian McDonald's omega

Bayesian McDonald's omega applies Bayesian statistical estimation to the omega reliability coefficient, yielding a full posterior distribution over omega rather than a single point estimate. This provides credible intervals and probabilistic uncertainty quantification for the reliability of a composite or scale score,

2 zdrojů1999
psychometrics

Bayesian Measurement Invariance

Bayesian measurement invariance testing evaluates whether a scale's factor loadings and item intercepts are equivalent across groups, using a Bayesian framework that allows parameters to deviate from strict equality by a small, probabilistically specified amount rather than imposing an exact constraint.

2 zdrojů2013
bioinformatics

Bayesian Metabolomics Analysis

Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributio

2 zdrojů2005
machine learning

Bayesian Metric Learning

Bayesian Metric Learning frames the problem of learning a task-adapted distance function as probabilistic inference. Rather than producing a single optimal metric matrix, it places a prior over metrics, updates it with pairwise similarity or label constraints, and yields a posterior distribution that quantifies uncerta

2 zdrojů2010
bioinformatics

Bayesian Microbiome Diversity Analysis

Bayesian microbiome diversity analysis applies probabilistic models — chiefly Dirichlet-Multinomial and related hierarchical frameworks — to 16S rRNA or shotgun metagenomic count data to estimate alpha-diversity (within-sample richness and evenness) and beta-diversity (between-sample compositional differences) while pr

2 zdrojů2010
simulation

Bayesian Microsimulation

Bayesian Microsimulation combines individual-level simulation of heterogeneous populations with Bayesian statistical inference. Each synthetic individual follows a probabilistic life path, while model parameters are governed by prior beliefs updated with observed data. This approach is widely used in health technology

2 zdrojů1990
statistics

Bayesian Mixed Effects Model

The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for bot

2 zdrojů1990
statistics

Bayesian Mixture Modeling

Bayesian mixture modeling represents the population as a weighted sum of K component distributions and estimates all unknowns — mixing weights, component parameters, and even the number of components — through posterior inference. It extends classical mixture analysis by placing priors on every parameter and quantifyin

2 zdrojů1997
bayesian

Bayesian Model Averaging

Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the

2 zdrojů1999
bayesian

Bayesian Model Averaging with Measurement Error

Bayesian model averaging with measurement error (BMA-ME) combines two probabilistic ideas: it averages predictions across competing regression models weighted by each model's posterior probability, while simultaneously accounting for the fact that one or more predictors are observed with random error rather than exactl

2 zdrojů1999
bayesian

Bayesian model averaging with missing data

Bayesian Model Averaging with missing data (BMA-MD) simultaneously addresses two sources of uncertainty: which model best describes the data, and what the unobserved values are. Rather than selecting a single imputed dataset and a single model, the approach averages predictions across the full space of candidate models

2 zdrojů1999
research design

Bayesian Model Testing Research

Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unl

2 zdrojů1935
statistics

Bayesian Moderated Mediation

Bayesian moderated mediation estimates how a mediator transmits the effect of a predictor onto an outcome, and whether that indirect effect varies in size depending on a moderator variable — all within a Bayesian framework that quantifies uncertainty via posterior distributions rather than p-values and confidence inter

2 zdrojů2009
statistics

Bayesian Moderation Analysis

Bayesian moderation analysis tests whether the relationship between a predictor and an outcome changes depending on the value of a third variable (the moderator). By placing prior distributions on all model parameters and updating them with observed data, it yields full posterior distributions for the interaction effec

2 zdrojů2000
simulation

Bayesian Monte Carlo Simulation

Bayesian Monte Carlo Simulation integrates Bayesian statistical inference with Monte Carlo sampling to propagate uncertainty through complex models. Instead of drawing samples from arbitrary distributions, it conditions sampling on observed data and expert prior knowledge via Bayes' theorem, yielding posterior-based un

2 zdrojů1987
spatial analysis

Bayesian Moran's I

Bayesian Moran's I embeds the classical Moran's I spatial autocorrelation test within a Bayesian probabilistic framework. Rather than producing a single p-value, it yields a posterior distribution over the spatial autocorrelation parameter, enabling uncertainty quantification, incorporation of prior knowledge, and more

2 zdrojů1950
simulation

Bayesian Multi-Objective Optimization

Bayesian Multi-Objective Optimization (BMOO/MOBO) uses Gaussian process surrogate models to approximate multiple expensive objective functions and guides the search toward the Pareto frontier with minimal real evaluations. By quantifying prediction uncertainty at each candidate point, it balances exploration of unknown

2 zdrojů2006
statistics

Bayesian Multidimensional Scaling

Bayesian Multidimensional Scaling places objects in a low-dimensional latent space so that inter-object distances reproduce observed dissimilarities, while a full Bayesian treatment quantifies uncertainty in the coordinates, handles missing proximities naturally, and selects the number of dimensions via model compariso

2 zdrojů2001
statistics

Bayesian Multinomial Logistic Regression

Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling

2 zdrojů1966
statistics

Bayesian Multiple Correspondence Analysis

Bayesian Multiple Correspondence Analysis extends classical MCA by embedding the geometric decomposition of categorical data tables within a Bayesian probabilistic framework, enabling principled uncertainty quantification around category coordinates, dimension selection via marginal likelihood, and incorporation of pri

2 zdrojů2000
statistics

Bayesian Multiple linear regression

Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seaml

2 zdrojů1971
network analysis

Bayesian Multiplex Network Analysis

Bayesian multiplex network analysis applies probabilistic generative modelling to networks that carry more than one type of relational tie simultaneously — such as friendship, collaboration, and communication links among the same set of actors. By placing priors over community memberships, edge probabilities, and layer

2 zdrojů2014
spatial analysis

Bayesian Multiscale Geographically Weighted Regression

Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterio

2 zdrojů2017
machine learning

Bayesian Naive Bayes

Bayesian Naive Bayes applies a fully Bayesian treatment to the parameters of the classic Naive Bayes classifier: instead of estimating class-conditional distributions by maximum likelihood, it places conjugate priors (typically Dirichlet for categorical data or Gaussian-Gamma for continuous data) over the parameters an

2 zdrojů1960
econometrics

Bayesian NARDL

Bayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior k

2 zdrojů2014
statistics

Bayesian Negative Binomial Regression

Bayesian Negative Binomial Regression models non-negative integer count outcomes that exhibit overdispersion — where the variance exceeds the mean — by placing a negative binomial likelihood on the data and specifying prior distributions over the regression coefficients and the dispersion parameter. Posterior inference

2 zdrojů1990
epidemiology

Bayesian nested case-control

A Bayesian nested case-control study embeds a case-control sampling scheme within a defined prospective cohort and then estimates exposure-outcome associations using Bayesian inference. Cases are individuals in the cohort who develop the outcome of interest; controls are sampled from the risk set at the time each case

2 zdrojů1977
bayesian

Bayesian Network

A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the

1 zdroj1988
network analysis

Bayesian Network Diffusion Analysis

Bayesian Network Diffusion Analysis applies Bayesian probabilistic inference to the study of how information, diseases, behaviors, or innovations propagate through a network. By placing priors over diffusion parameters and updating them with observed cascade data, it quantifies transmission rates, identifies influentia

2 zdrojů2010
bayesian

Bayesian Network with Measurement Error

A Bayesian network with measurement error is a probabilistic directed acyclic graphical model in which one or more node variables are observed with error rather than exactly. Latent true-value nodes are introduced for mismeasured variables, and the model jointly infers the network's conditional probability parameters a

2 zdrojů1988
bayesian

Bayesian Nonparametric Methods

Bayesian nonparametric methods are a family of flexible Bayesian models in which model complexity is not fixed in advance but grows automatically with the data. The two most widely used members are the Dirichlet Process Mixture (DPM), which clusters observations without pre-specifying the number of clusters, and Gaussi

2 zdrojů1973
research design

Bayesian Observational Quantitative Research

Bayesian observational quantitative research applies Bayesian statistical inference to data collected without experimental manipulation — surveys, administrative records, registries, or secondary datasets. Instead of relying solely on p-values and confidence intervals, the analyst encodes prior knowledge about paramete

2 zdrojů1990
econometrics

Bayesian OLS

Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior k

2 zdrojů1971
machine learning

Bayesian one-class SVM

Bayesian one-class SVM combines the classical one-class support vector machine — which learns a tight boundary around normal training examples — with Bayesian inference to produce calibrated probability estimates of anomaly, rather than only a binary flag. This allows uncertainty quantification over the novelty decisio

2 zdrojů2001
statistics

Bayesian one-sample t-test

The Bayesian one-sample t-test compares a single group's mean against a fixed reference value using a Bayes factor rather than a p-value. It quantifies the evidence the data provide for the null hypothesis (mean equals the reference) versus the alternative, and yields a full posterior distribution over the effect size

2 zdrojů2009
statistics

Bayesian one-way ANOVA

Bayesian one-way ANOVA tests whether the means of three or more independent groups differ by computing a Bayes factor — a ratio that quantifies how much more likely the data are under a model that allows group differences than under the null model that assumes equal means. Unlike the classical F-test, it provides direc

2 zdrojů1961
machine learning

Bayesian Online Learning

Bayesian online learning applies Bayesian inference sequentially: each time a new observation arrives, the current posterior over model parameters becomes the prior for the next update. The result is a principled probabilistic framework that maintains calibrated uncertainty estimates throughout, making it well-suited f

2 zdrojů1990
statistics

Bayesian Ordinal Logistic Regression

Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantif

2 zdrojů1999
spatial analysis

Bayesian Ordinary Kriging

Bayesian Ordinary Kriging is a geostatistical interpolation method that combines classical ordinary kriging with a Bayesian framework to jointly estimate the spatial covariance parameters and produce predictions at unsampled locations. Unlike plug-in kriging, it propagates uncertainty about variogram parameters through

2 zdrojů1993
network analysis

Bayesian PageRank

Bayesian PageRank extends the classic PageRank algorithm by embedding it within a Bayesian probabilistic framework. Instead of returning a single deterministic rank score for each node, it quantifies uncertainty over rank estimates — particularly valuable when the network is incomplete, noisy, or observed with error. I

2 zdrojů1999
econometrics

Bayesian Panel Data Analysis

Bayesian panel data analysis applies Bayesian inference to models with repeated observations on multiple units. By placing prior distributions on coefficients and variance components, it merges prior knowledge with the observed panel likelihood to produce full posterior distributions for fixed or random effects, slope

2 zdrojů1971
causal inference

Bayesian Panel Event Study

Bayesian Panel Event Study is a causal inference design that estimates dynamic treatment effects around a datable event using panel data, replacing classical frequentist estimation with Bayesian posterior inference. It produces period-by-period effect estimates with full probability distributions, enabling principled u

2 zdrojů2010
research design

Bayesian Panel Research

Bayesian panel research combines the longitudinal structure of panel data — where the same units (individuals, firms, countries) are observed at multiple time points — with Bayesian statistical inference. Rather than relying solely on the observed data and point estimates, it incorporates prior knowledge via probabilit

2 zdrojů1990
bioinformatics

Bayesian Pathway Enrichment Analysis

Bayesian pathway enrichment analysis tests whether a predefined set of genes — a biological pathway — is systematically overrepresented among genes that show evidence of differential activity in an experiment. Unlike classical over-representation tests, it encodes prior biological knowledge as a prior distribution and

2 zdrojů2001
epidemiology

Bayesian Phase I clinical trial

A Bayesian Phase I clinical trial uses prior probability models and sequential Bayes updating to find the maximum tolerated dose (MTD) of a new agent. Unlike the traditional 3+3 rule-based escalation, the Bayesian approach revises a dose-toxicity curve continuously as each patient's outcome is observed, allowing faster

2 zdrojů1990
epidemiology

Bayesian Phase II Clinical Trial

A Bayesian Phase II clinical trial applies Bayesian statistical inference to the standard Phase II objective of evaluating whether an experimental treatment shows sufficient early-phase efficacy to justify progression to a Phase III trial. By combining prior information with accumulating trial data, it enables principl

2 zdrojů1990
epidemiology

Bayesian Phase III Clinical Trial

A Bayesian Phase III clinical trial is a large-scale, confirmatory randomized controlled trial that uses Bayesian statistical inference rather than conventional frequentist hypothesis testing to evaluate whether an experimental treatment meets pre-defined efficacy and safety thresholds. By combining prior evidence with

2 zdrojů1990
epidemiology

Bayesian Phase IV study

A Bayesian Phase IV study is a post-marketing research design that applies Bayesian statistical inference to accumulate evidence about a drug or device already approved for clinical use. By formally combining prior evidence from earlier development phases with emerging real-world data, it enables continuous, probabilis

2 zdrojů1980
bioinformatics

Bayesian Phylogenetic Analysis

Bayesian phylogenetic analysis uses Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling to estimate the posterior probability distribution over phylogenetic trees and model parameters given observed sequence data. Unlike bootstrapped maximum-likelihood methods that return a single best tree, Bayesian inference

2 zdrojů1996
causal inference

Bayesian Placebo Test

The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a m

2 zdrojů2010
statistics

Bayesian Poisson Regression

Bayesian Poisson regression models non-negative integer count outcomes using a Poisson likelihood with a log link, placing prior distributions on the regression coefficients. Posterior inference — combining prior beliefs with the data likelihood — produces full probability distributions over the coefficients rather tha

2 zdrojů1989
statistics

Bayesian Power Analysis

Bayesian power analysis — also called assurance — is a sample size determination method that replaces the frequentist notion of power with a probability-weighted average over a prior distribution on the effect size. First formalised by Spiegelhalter and Freedman (1986) and further developed by O'Hagan, Stevens and Camp

2 zdrojů1986
econometrics

Bayesian PP unit root test

The Bayesian Phillips-Perron unit root test combines the nonparametric long-run variance correction of the classical Phillips-Perron test with a Bayesian inferential framework. Instead of a p-value, it yields a posterior probability or Bayes factor quantifying evidence for or against a unit root, allowing researchers t

2 zdrojů1988
statistics

Bayesian Principal Component Analysis

Bayesian principal component analysis embeds probabilistic PCA within a Bayesian framework, placing priors over the loading matrix so that irrelevant components are automatically pruned. It handles missing data naturally and provides principled uncertainty estimates for both the latent scores and the dimensionality of

2 zdrojů1999
statistics

Bayesian Probit model

The Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns prior distributions to regression coefficients and updates them with observed data, yielding a full posterior distribution rather than a sing

2 zdrojů1993
experimental design

Bayesian Process Capability Analysis

Bayesian Process Capability Analysis integrates Bayesian inference with classical capability indices (Cp, Cpk, Cpm) to estimate how well a production process meets specification limits. Rather than relying solely on observed sample data, it incorporates prior knowledge about process parameters — yielding more stable an

2 zdrojů1986
causal inference

Bayesian Propensity Score Matching

Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account

2 zdrojů2012
causal inference

Bayesian Propensity Score Weighting

Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yiel

2 zdrojů2009
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