) is one of the most economically important fish species in the East ChinaSea and supports one of the most valuable fisheries in China. From 1990 to 2012, the total catch for this fisheryranged from 0.39 to 0.91 million tons. However, most studies on this fishery concentrated on feeding habit, variationsof catches, trophic composition, and the stock-recruitment relationship. For management, yield per recruitand surplus production models were applied to analyze the data of this fishery and provide a rough MSY estimateof approximately 7.5tons. Until now, reports on the use of stock assessment models for this fishery are limited,and no uncertainty assessment has been undertaken. Therefore, Bayesian state-space modelling was appliedto the catch and catch per unit effort(CPUE) data for this fishery. A state-space model describes the dynamics oftwo related processes: the observation process, which is a function of the unobserved state process, and the stateprocess, which describes the unobserved population dynamics in terms of biomass or abundance. In the presentstudy, the PellaTomlinson surplus production model was used for the state process. We used Bayesian inferencebecause it can take into account more uncertainties that are linked to parameters. In this study, four models wereconstructed based on Markov Chain Monte Carlo simulation with a mix of information and non-information priors.Marginal posterior distributions of model parameters, biological reference points (BRPs), and unobserved variableswere based on 250000 iterations after discarding the first 50000 burn-in iterations to ensure no persistentinitial pathologic behavior. Results showed that the best-fit of the four models was model 1, with lognormal priorsfor the intrinsic rate of increase ’s method was applied for convergence diagnostics, and WINBUGS software computed the results of theautocorrelation diagnostics. The parameters in model 1 were best fit and passed all the diagnostics. The prior distributionshad a significant impact on the results of , which indicates that the data are sensitive to the typeof prior distributions of indicate that the data provide more information than the prior distribution for Bayesian analysis. BRP resultsshowed that hairtail stock was overfished from 1995 to 2010 (catch over maximum sustainable yield) and faced aserious threat from 2000 to 2006 (fishing mortality coefficient over ). The stock was in a good state in 2012but required persistent management. Because of possible statistical distortion, the results of MSY and may beoverrated. The estimated results from 2004 to 2012 also have uncertainties, because the hairtail fishery in the EastChina Sea was also influenced by monsoon, precipitation, and other environmental factors.