116 research outputs found
ModÚles à espace d'états non linéaires/non gaussiens et inférence bayésienne par méthode {MCMC} -- Une application en évaluation des stocks halieutiques
National audienceDifference equations with delay are widely used to model the evolution of the biomass of a fish stock (delay difference models). Represented as a state- space model they allow, starting from the data of the annual catches, a relevant Bayesian analysis. For this purpose we can use an hybrid MCMC method combi- ning a Metropolis-Hastings algorithm within a Gibbs sampler, namely the single- component Metropolis-Hastings algorithm
The freedom to choose: integrating communitybased reproductive health services with locally led marine conservation initiatives in southwest Madagascar
Madagascarâs diverse marine ecosystems serve as critical biodiversity habitats and are also essential to the livelihoods, food security and culture of coastal people, including semi-nomadic Vezo fishers based along the southwest coast. Commercialisation of their traditional fisheries, rapid coastal population growth related to unmet family planning needs, and lack of alternatives to fishing in this arid region are resulting in the unsustainable exploitation of coastal resources. In response to these challenges, marine conservation organisation Blue Ventures has developed an approach to community-based conservation and development that reflects the inextricable links between humans, their health and the environment. We describe how this model has evolved in the Velondriake locally managed marine area, home to approximately 1 0,000 people, over the last decade through strong cross-sector partnerships. It has entailed the integration of community-based reproductive health services with locally led marine conservation initiatives including temporary octopus fishery closures, permanent marine reserves and alternative coastal livelihood activities such as aquaculture. All of these programmes are underpinned by community education that engages men, women, youth and children in both health and conservation topics. The provision of voluntary family planning services in the Velondriake area is estimated to have averted more than 800 unintended pregnancies since 2007, and the temporary octopus fishery closure model has been implemented over 1 50 times along the southwest coast since 2004. Preliminary, anecdotal reports from community members and programme staff indicate that this integrated Population-Health-Environment approach enables couples to plan and better provide for their families, empowers women, improves food security and directly supports the sustainability of local conservation efforts. It is proving to be an easily replicable model for addressing community health needs and advancing biodiversity conservation efforts in some of Madagascarâs most remote and under-served areas. Non seulement les Ă©cosystĂšmes marins de Madagascar abritentils une biodiversitĂ© exceptionnelle mais ils sont Ă©galement intrinsĂšquement liĂ©s au mode de vie et Ă la sĂ©curitĂ© alimentaire des populations cĂŽtiĂšres, notamment des pĂȘcheurs seminomades qui vivent le long de la cĂŽte sud-ouest. La commercialisation des produits de la pĂȘche traditionnelle, la croissance rapide de la population qui est en partie liĂ©e Ă des dĂ©fauts en matiĂšre de planification familiale et lâabsence dâalternatives Ă la pĂȘche dans cette rĂ©gion aride se traduisent par une exploitation non durable des ressources cĂŽtiĂšres. Pour trouver une solution Ă cette situation, l'organisation de conservation marine Blue Ventures a Ă©laborĂ© une approche holistique qui considĂšre les liens obligĂ©s entre les Hommes, leur santĂ© et l'environnement. LâĂ©volution du modĂšle Ă©laborĂ© pour lâaire marine de Velondriake est dĂ©crite ici; elle concerne environ 10 000 personnes au cours de cette derniĂšre dĂ©cennie et des partenariats multisectoriels. Le modĂšle a intĂ©grĂ© des services de santĂ© reproductive avec des initiatives de conservation marine gĂ©rĂ©es localement, comme des fermetures temporaires de la pĂȘche aux poulpes, des rĂ©serves marines permanentes et des activitĂ©s gĂ©nĂ©ratrices de revenus telles que l'aquaculture. Lâensemble de ces programmes est soutenu par des actions dâĂ©ducation en mobilisant les hommes, femmes, jeunes et enfants sur des thĂšmes aussi variĂ©s que la santĂ© ou la protection de lâenvironnement. Ainsi, on estime que la prestation des services de planification familiale volontaire dans la rĂ©gion de Velondriake a pu Ă©viter plus de 800 grossesses non dĂ©sirĂ©es depuis 2007, et des fermetures temporaires de la pĂȘche aux poulpes plus de 1 50 fois le long de la cĂŽte sud-ouest depuis 2004. Des rapports prĂ©liminaires et anecdotiques de membres des communautĂ©s et des personnels du programmes indiquent que cette approche intĂ©grĂ©e « SantĂ© â Population â Environnement » permet aux couples de planifier et de mieux subvenir aux besoins de leurs familles, aux femmes de sâĂ©manciper et aux familles dâamĂ©liorer leur sĂ©curitĂ© alimentaire tout en soutenant directement la durabilitĂ© des activitĂ©s de conservation marine gĂ©rĂ©es localement. Ce modĂšle sâest rĂ©vĂ©lĂ© ĂȘtre facilement reproductible pour rĂ©pondre aux besoins de santĂ© communautaire et pour faire avancer les efforts de conservation de la biodiversitĂ© dans les rĂ©gions les plus reculĂ©es et les plus isolĂ©es de Madagascar
Analyse bayésienne de modÚles markoviens d'évolution de ressources naturelles
International audienceOne applies Monte Carlo methods to state sapce models with unknown parameters. The first one is a Monte Carlo Markov Chain algorithm. The second one is the particle filtering. We compare these methods applied to a biomass evolution model for fisheries
Using rewriting techniques to produce code generators and proving them correct
AbstractA major problem in deriving a compiler from a formal definition is the production of correct and efficient object code. In this context, we propose a solution to the problem of code-generator generation.Our approach is based on a target machine description where the basic concepts used (storage classes, access modes, access classes and instructions) are hierarchically described by tree patterns. These tree patterns are terms of an abstract data type. The program intermediate representation (input to the code generator) is a term of the same abstract data type.The code generation process is based on access modes and instruction template-driven rewritings. The result is that each program instruction is reduced to a sequence of elementary machine instructions, each of them representing an instance of an instruction template.The axioms of the abstract data type are used to prove that the rewritings preserve the semantics of the intermediate representation
Bayesian numerical inference for hidden Markov models
International audienceIn many situations it is important to be able to propose N independent real- izations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains (MCMC) interact in order to get an approximation of an indepen- dent N-sample of a given target law. In this method each individual chain proposes can- didates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model
Bayesian numerical inference for Markovian models -- Application to tropical forest dynamics
International audienceBayesian modelling is fluently employed to assess natural ressources. It is associated with Monte Carlo Markov Chains (MCMC) to get an approximation of the distribution law of interest. Hence in such situations it is important to be able to propose N independent realiza- tions of this distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains interact in order to get an approximation of an independent N -sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through example that it possesses many advantages. This approach will be applied to a forest dynamic model
A Markov model of land use dynamics
The application of the Markov chain to modeling agricultural succession is
well known. In most cases, the main problem is the inference of the model, i.e.
the estimation of the transition matrix. In this work we present methods to
estimate the transition matrix from historical observations. In addition to the
estimator of maximum likelihood (MLE), we also consider the Bayes estimator
associated with the Jeffreys prior. This Bayes estimator will be approximated
by a Markov chain Monte Carlo (MCMC) method. We also propose a method based on
the sojourn time to test the adequation of Markov chain model to the dataset
Bayesian numerical inference for Markovian models -- Application to tropical forest dynamics
International audienceBayesian modelling is fluently employed to assess natural ressources. It is associated with Monte Carlo Markov Chains (MCMC) to get an approximation of the distribution law of interest. Hence in such situations it is important to be able to propose N independent realiza- tions of this distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains interact in order to get an approximation of an independent N -sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through example that it possesses many advantages. This approach will be applied to a forest dynamic model
ModÚle markovien d'octroi de crédit en microfinance
ABSTRACT. Starting from the generalized model of Osman Khodr and Francine Diener [1], we present a new model that meets the expectations of the microfinance institution (MFI) and that of the borrowers and that incorporates all the characteristics of the poor, namely tolerance in case of partial default and the possibility of having a progressive loan automatically. This model will provide microfinance institutions with a decision support tool that is better adapted to the reality of microfinance. Our Markov chain consists of several statements associated with the economic status of the borrower including three types of recipients B 1 (state of being beneficiary at a time t = 0), B 2 (state to be beneficiary at a time t = 1) and I (state of financial inclusion: permanent beneficiary), an applicant state A 1 and A T â1 ((T â 1) excluded states). We modeled a borrower's behavior by a λ parameter that depends on the borrower's α probability of success. At the initial time, λ = 1+α 1âα , this quantity changes as soon as the borrower moves from one state to another with a probability of success different from α. The agency's decision to grant a credit depends entirely on the λ parameter which is compared to the set subjective threshold-values. The chance Îł to have a loan (Îł: probability of credit request granted) for a borrower depends on the parameter λ, with Îł = 1 â 1 λ. keywords: Microfinance, Credit Grant Decision, Markov Chain, Individual Loan, Dynamic Incentive, Updated Expected ProfitEn partant du modĂšle gĂ©nĂ©ralisĂ© de Osman Khodr et Francine Diener [1], nous prĂ©sentons un nouveau modĂšle qui rĂ©pond aux attentes de l'institution de microfinance (IMF) et celle des emprunteurs et qui incorpore toutes les caractĂ©-ristiques des populations pauvres, Ă savoir la tolĂ©rance en cas de dĂ©faut partiel et la possibilitĂ© d'avoir un prĂȘt progressif de façon automatique. Ce modĂšle offrira aux institutions de microfinance un outil d'aide Ă la dĂ©cision plus adaptĂ© Ă la rĂ©alitĂ© de la microfinance. Notre chaĂźne de Markov comprend plusieurs Ă©tats associĂ©s Ă la situation Ă©conomique de l'emprunteur dont trois types de bĂ©nĂ©ficiaires B 1 (Ă©tat d'ĂȘtre bĂ©nĂ©ficiaire au temps t = 0), B 2 (Ă©tat d'ĂȘtre bĂ©nĂ©ficiaire au temps t = 1) et I (Ă©tat d'inclusion financiĂšre: bĂ©nĂ©ficiaire permanent), un Ă©tat de demandeur A 1 et A T â1 ((T â 1) Ă©tats d'exclus). Nous avons modĂ©lisĂ© le comportement d'un emprunteur par un paramĂštre λ qui dĂ©pend de la probabilitĂ© α de rĂ©ussite de l'emprunteur. A l'instant initial, λ = 1+α 1âα , cette quantitĂ© change dĂšs que l'emprunteur passe d'un Ă©tat Ă un autre avec une probabilitĂ© de rĂ©ussite diffĂ©rente de α. La dĂ©cision de l'agence d'accorder un crĂ©dit dĂ©pend entiĂšrement du paramĂštre λ qui est comparĂ© aux valeurs-seuils subjectives fixĂ©es. La chance Îł d'avoir un prĂȘt (Îł: probabilitĂ© de demande de crĂ©dit accordĂ©e) pour un emprunteur est fonction du paramĂštre λ, avec Îł = 1 â 1 λ. MOTS-CLĂS : Microfinance, DĂ©cision d'octroi de crĂ©dit, chaĂźne de Markov, PrĂȘt individuel, Incitation dynamique, Profit espĂ©rĂ© actualis
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