Above are papers related to the following paper: [Paper_ID] Paper #646412. 	[Title] A NEW SEQUENTIAL SIMULATED ANNEALING METHOD [Abstract] Let H be a function not explicitly defined, but approximable by a sequence (H n ) n0 of functional estimators. In this context we propose a new sequential algorithm to optimise asymptotically H using stepwise estimators H n . We prove under mild conditions the almost sure convergence in law of this algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131330. 	[Title] A SEQUENTIAL METROPOLIS-HASTINGS ALGORITHM [Abstract] This paper deals with the asymptotic properties of the Metropolis-Hastings algorithm, when the distribution of interest is unknown, but can be approximated by a sequential estimator of its density. We prove that, under very simple conditions, the rate of convergence of the Metropolis-Hastings algorithm is the same as that of the sequential estimator when the latter is introduced as the reversible measure for the Metropolis-Hastings Kernel. This problem is a natural extension of previous a work on a new simulated annealing algorithm with a sequential estimator of the energy. Please summarise all the given information.

Paper #646412 has the title `A NEW SEQUENTIAL SIMULATED ANNEALING METHOD  
` and the abstract `Let H be a function not explicitly defined, but approximable by a sequence (H n ) n0 of functional estimators. In this context we propose a new sequential algorithm to optimise asymptotically H using stepwise estimators H n . We prove under mild conditions the almost sure convergence in law of this algorithm. 
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Question: Which category should Paper #646412 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #646412 has the title `A NEW SEQUENTIAL SIMULATED ANNEALING METHOD  
` and the abstract `Let H be a function not explicitly defined, but approximable by a sequence (H n ) n0 of functional estimators. In this context we propose a new sequential algorithm to optimise asymptotically H using stepwise estimators H n . We prove under mild conditions the almost sure convergence in law of this algorithm. 
`.
Question: Which category should Paper #646412 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods