This (ugly) webpage presents a list of references, codes and videolectures available for SMC/particle filters. It is by no means exhaustive and obviously biased towards my work and the work of my close colleagues. A complementary site for SMC and Particle filters resources by Pierre Del Moral can be found here.
I keep on adding stuff from time to time, although not as often as I should.
References
Textbooks
* G. Kitagawa & W. Gersch, Smoothness Priors Analysis of Time Series, Lecture Notes in Statistics, Springer, 1996. 
The first book I am aware of discussing particle filters, applications
to spectral analysis, changepoints etc and it is also a nice
introduction to statespace models.
* P. P. Del Moral & L. Miclo, Branching and Interacting
Particle Systems Approximations of FeynmanKac Formulae with
Applications to Nonlinear Filtering, Seminaire de Probabilites,
Lecture Notes in Mathematics, SpringerVerlag Berlin, vol. 1729, pp.
1145, 2000. Ps 
The first review of theoretical results behind SMC, the books by Del
Moral (2004, 2013 see below) are much more complete (but these lecture notes
are free, although the books are also free on some websites).
* A.D., N. De Freitas & N.J. Gordon (editors), SMC Methods in Practice, SpringerVerlag, 2001.  Large collection of chapters on the subject, a bit outdated now but good to start with.
* P. Del Moral, FeynmanKac Formulae: Genealogical and Interacting Particle Approximations, SpringerVerlag, 2004. 
Everything you want to know about the theory behind SMC, also includes
nice nonstandard applications. The notation can appear a little bit
overwhelming at the beginning but anyone who has worked on the subject
learn to appreciate how powerful they are eventually.
* P. Del Moral, Mean Field Simulation for Monte Carlo Integration, Chapman & Hall  CRC, 2013.  A significantly updated and expanded version of his previous 2004 monograph.
* O. Cappe, E. Moulines & T. Ryden, Inference in Hidden Markov Models, SpringerVerlag, 2005.  A comprehensive treatment of hidden Markov models which includes a few chapters on SMC methods.
* S. Särkkä, Bayesian Filtering and Smoothing, CUP, 2013. See dedicated site and online  An introduction to advanced nonlinear filtering methods including SMC supported by Matlab examples. Basic Introduction to SMC for
statespace models
* A.D., N. De Freitas and N.J. Gordon, An introduction to
Sequential Monte Carlo Methods, in SMC in Practice, 2001 Pdf  Simple introduction to basic SMC methods for statespace models.
Early papers
* L.
Stewart, P. McCarty, The use of Bayesian Belief Networks to fuse
continuous and discrete information for target recognition and discrete
information for target recognition, tracking, and situation assessment,
in Proc. SPIE Signal Processing, Sensor Fusion and Target Recognition,, vol. 1699, pp. 177185, 1992. Pdf (see pages 171176 in acrobat). 
First paper I am aware of introducing what is now known as the
bootstrap filter, this paper has only been cited 8 times since
1992! (at least I cited it 3 times).
* N.J. Gordon, D. Salmond and A.F.M. Smith, Novel approach to nonlinear/nonGaussian Bayesian state estimation, IEE Proc. F, 1993 Pdf  The seminal paper introducing SMC for filtering.
* G. Kitagawa, Monte Carlo filter and smoother for nonGaussian nonlinear statespace models, JCGS,
1996  Journal version of A Monte Carlo Filtering and Smoothing Method
for NonGaussian Nonlinear State Space Models published in 1993 in
the Proceedings of the 2nd U.S.Japan Joint Seminar on Statistical Time Series Analysis,
pp. 110131. This 1993 paper introduced particle filters at the same time as Gordon,
Salmond & Smith but it has been unfairly
forgotten. First introduction of stratified resampling.
* M. Hurzeler and H. Kunsch, Monte Carlo approximations for general statespace models, JCGS, 1998. 
Propose to address the filtering and smoothing problems using Monte
Carlo: not quite SMC as implemented nowadays as rejection
sampling is used to sample from a mixture of distributions.
* J.S. Liu and R. Chen, Sequential Monte
Carlo methods for dynamic systems, JASA, 1998 Pdf 
This paper shows that SMC goes far beyond statespace models and are
applicable to any sequence of distributions of increasing dimension.
* M.K. Pitt and N. Shephard, Filtering via Simulation: Auxiliary
Particle Filter, JASA, 1999 Pdf  This paper introduces the popular auxiliary particle filter and perfect adaptation.
* J. Carpenter, P. Clifford and P. Fearnhead, An Improved Particle Filter for Nonlinear Problems, IEE Proc. F, 1999 Pdf  This paper presents an improved version of auxiliary particle filters and stratified resampling.
* A.D., S.J. Godsill and C. Andrieu, On Sequential Monte Carlo
sampling methods for Bayesian filtering, Stat. Comp., 2000 Pdf  This paper presents the "optimal" importance distribution, ways to approximate it, smoothing and RaoBlackwellization.
Tutorials papers
* H. Kunsch, State space and hidden Markov models, Chapter 3 of Complex Stochastic
Systems, O. E. BarndorffNielsen, D. R. Cox and C. Klüppelberg, eds.,
CRC Press, 109173, 2001. 
Present a nice survey of HMM, statespace and Monte Carlo
approximations as of 2001. Also discusses the connections with the
reference measure approach favoured by Elliott et al.
*
S. Arulampalam, S. Maskell, N.J. Gordon & T. Clapp, A
Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian
Tracking, IEEE Trans. Sig. Proc., 2002 Pdf 
Popular tutorial but a bit outdated now, e.g. does not include
resamplemove, smoothing or RaoBlackwellisation. Could be a good start
to understand SMC in a statespace model context though.
* O. Cappe, S.J. Godsill & E. Moulines, An Overview of Existing Methods and Recent Advances in SMC, Proc. IEEE, 2007  This paper also discusses resample move, some smoothing techniques and RaoBlackwellisation.
* D. Creal, A Survey of Sequential Monte Carlo Methods for Economics and Finance, Econometric Reviews, to appear Pdf  Tutorial introducing SMC and its applications in an economics and finance context.
* P. Del Moral, F. Patras, & S. Rubenthaler, A Mean Field Theory of Nonlinear Filtering, in the Oxford Handbook of Nonlinear Filtering, Oxford University Press, 2011 Pdf  A more theoretically oriented tutorial that presents some of the main results in the area.
* P. Del Moral & A.D., Particle Methods: An Introduction with Applications, ESAIM Proceedings, 2014 Pdf 
Discuss particle methods in a general (nonnecessarily filtering
context) and presents the proofs of simple theoretical results.
* A.D. & A.M. Johansen  A Tutorial on Particle Filtering and Smoothing: 15 Years Later, in the Oxford Handbook of Nonlinear Filtering, Oxford University Press, 2011 Pdf 
This paper shows that most SMC algorithms including auxiliary particle filters,
resamplemove, block sampling etc. can be reinterpreted within a simple unified framework.
* N.
Kantas, A.D., S.S. Singh, J.M. Maciejowski and N. Chopin, On
Particle Methods for Parameter Estimation in StateSpace Models. Statistical Science, 2015. PdfMatlab code 

Tutorial detailing the pros and cons of particle methods for static
parameter estimation. Upated version of An overview of
sequential Monte Carlo methods for parameter estimation in general
statespace models, in Proceedings IFAC System Identification (SySid)
Meeting, 2009.
Fighting degeneracy: Using MCMC steps & lookahead strategies
A
wellknown problem with SMC approximations is that they suffer from the
degeneracy problem, i.e. as time increases the approximations of the
"earlier" marginal distributions collapse. You can try to mitigate (but
not eliminate) this problem using either some MCMC moves as suggested
by Gilks & Berzuini or by using lookahead strategies of which
my favourite is block sampling.
*
W. Gilks & C. Berzuini, Following a moving target: Monte Carlo
inference for dynamic Bayesian models, JRSS B,
2001  This paper proposes to move the particles using MCMC moves with the
appropriate invariant distribution so as to introduce diversity among
particles.
* A.D., M. Briers & S. Senecal, Efficient
Block Sampling Strategies for SMC, JCGS, 2006
Pdf  This pape shows how it is possible to potentially drastically reduce
the number of resampling steps, hence the degeneracy, by sampling
blocks of state variables. Can be thought of as a block version of
auxiliary particle filters.
* M. Lin, R. Chen & J.S. Liu, Lookahead Strategies for SMC, Statistical Science 2012 Pdf  This paper reviews various lookahead strategies to mitigate degeneracy.
Reducing the Variance
 using RaoBlackwellization Whenever
you can compute an integral analytically, then do it and avoid Monte
Carlo. An obvious principle one can put in practice for a wide range of
statespace models of interest.
* C. Andrieu & A.D., Particle Filtering for Partially Observed
Gaussian State Space
Models, JRSS B, 2002. Pdf  This paper shows how the Kalman filter can be use to compute the prior of a
latent process, particularly useful for dynamic probit/tobit models.
* R. Chen & J. Liu, Mixture Kalman filters, JRSS B, 2000. Pdf 
This paper shows that for conditionally linear Gausian models which includes
switching statespace models, it is possible to devise a particle
filter which is a mixture of Kalman filters. * A.D., S.J. Godsill & C. Andrieu, On Sequential Monte Carlo
sampling methods for Bayesian filtering, (section IV) Stat. Comp., 2000
Pdf  Section IV of this paper presented independently the same material as Chen & Liu (2000).
*
K.P. Murphy, A.D., N. De Freitas & S.
Russell, RaoBlackwellised Particle Filtering for Dynamic Bayesian
Networks, in Proc. Uncertainty in Artificial Intelligence, 2000. Pdf  This paper uses RaoBlackwellization techniques to do efficient inference in high
dimensional dynamic Bayes nets.
* P. Fearnhead, P. & P. Clifford (2003). Online inference for hidden Markov models via particle filters. JRSS B, 2003. 
This paper shows that for discretevalued latent processes, standard particle
filters are inefficient and proposes an alternative approach that
bypasses the sampling step. Demonstrate its use for switching
statespace models.
* T. Schon, F. Gustafsson & P. Nordlund. Marginalized particle filters for mixed linear/nonlinear statespace models. IEEE Trans. Sig. Proc., 2005. Pdf  This paper proposes some generalizations of the RaoBlackwellized particle filters.
using Quasi Monte Carlo
* M. Gerber and N. Chopin, Sequential Quasi Monte Carlo, JRSS B (with discussion), 2015.Pdf  This
paper shows that you can use QMC within SMC to reduce the variance of
your estimates (at least for lowdimensional statespace). This is by
no mean trivial and the paper achieves this by the clever use of a Hilbert spacefilling curve.
Parallel implementation
* A. Lee et al., On the utility of graphics cards to perform massively parallel implementation of advanced Monte Carlo, JCGS, 2010. Website with CUDA codePdf  This paper discusses the use of GPU for the implementation of SMC methods.
* A. Lee & N.Whitely, Forest resampling for distributed SMC, Statistical Analysis and Data Mining, 2015 Pdf  SMC
are not so easy to parallelize as the resampling operation is a
bottleneck. This paper provides practical and principled ways to
perform resampling on a distributed computing architecture.
SMC Methods on Trees, Partially Ordered Sets, Combinatorial Spaces
* L. Wang, A. BouchardCote & A.D., Bayesian phylogenetic inference using a combinatorial SMC, JASA, 2015 Pdf 
Assume you want to do SMC not on statespace models but say for
nonclock trees or more generally on some combinatorial space. We
present a method to deal with this here relying on the existence of a
partially ordered set structure (poset).
SMC Smoothing
In
the context of statespace models, it is often of interest to perform
smoothing. Standard SMC approximations do provide an estimate of
the joint smoothing distributions but it is poor because of the
degeneracy problem aforementioned. Specific smoothing procedures have
been proposed to address this.
* A.D., S.J. Godsill & C. Andrieu, On Sequential Monte Carlo
sampling methods for Bayesian filtering, Stat. Comp., 2000 Pdf 
This paper describes an SMC implementation of forward
filteringbackward smoothing to compute marginal smoothing
distributions.
* G. Kitagawa & S. Sato, Monte Carlo Smoothing and Selforganising StateSpace Model. In SMC in Practice,
2001.  This paper proposes to approximate fixedinterval marginal smoothing
distributions by fixedlag marginal smoothing distributions to reduce
drastically the degeneracy problem.
* S.J. Godsill, A.D. & M. West, Monte Carlo Smoothing for Nonlinear Time Series, JASA,
2004 Pdf  This paper describes SMC implementation of the forward
filteringbackward sampling procedure to obtain samples approximately
from the joint smoothing distribution, cost O(NT) per path for N particles and T data.
* P. Del Moral, A.D. & S.S. Singh, Forward Smoothing using SMC, Technical report
Cambridge University TR 638, Sept. 2009, revised 2010 Pdf.
 This paper describes an SMC implementation of the forward filteringbackward
smoothing to compute expectations of additive functionals that bypasses
entirely the backward pass, presents theoretical results and applied it
to online parameter estimation using online gradient and online
EM.
* M. Briers, A.D. & S. Maskell, Smoothing Algorithms for Statespace Models, Ann. Instit. Stat. Math.,
2010 Pdf 
This paper presents a generalized version of the twofilter smoothing
formula which can be readily implemented using SMC methods to compute
marginal smoothing distributions and sample approximately from the
joint. Direct implementation has complexity O(N^2.T) and proposed
rejection sampling method yields O(N.T) (in compact spaces...) to
compute marginals, O(T) to sample approximately from the joint.
* P. Fearnhead, D. Wyncoll & J. Tawn, A Sequential Smoothing Algorithm with Linear Computational Cost, Biometrika,
2010 Pdf  This paper describes an importance sampling procedure to reduce computational
complexity of SMC implementation of direct implementation of
generalized twofilter formula to O(N.T) to compute marginals.
*
R. Douc, A. Garivier, E. Moulines & J. Olsson, On the Forward
Filtering Backward Smoothing Particle Approximations of the Smoothing
Distribution, Ann. Applied Proba,
to appear Pdf  This paper describes a rejection sampling method to implement forward filtering
backward sampling, cost O(T) per path and presents various theoretical
results.
SMC for online Bayesian static parameter estimation in statespace models
It is tempting to do online Bayesian static parameter estimation in
statespace models using SMC and MCMC moves. Unfortunately, all these
methods suffer from the path degeneracy problems so should be used
cautiously. They are definitely unreliable for big datasets and/or vague priors.
* P. Fearnhead, MCMC, sufficient statistics and particle filters, JCGS,
2002 Pdf  This is a journal paper version of a chapter of the D.Phil of
Fearnhead (1998) where it is proposed for the first time to use MCMC
moves on static parameters to mitigate the degeneracy problem. The
author clearly acknowledges that this does not entirely solve the
problem; see the discussion.
* C. Andrieu, N. De Freitas and A.D., Sequential MCMC for Bayesian
Model Selection, Proc. IEEE Workshop HOS, 1999 Pdf 
This paper presents an SMC algorithm for online Bayesian parameter
estimation for autoregressive parameters with unknown order using reversible jump MCMC moves.
It is explicitly mentioned at the end of the paper and demonstrated
experimentally that such methods are bound to suffer from the
degeneracy problem.
* G. Storvik, Particle filters for statespace models with the
presence of unknown static parameters, IEEE Trans. Signal Processing,
2002 Pdf  This paper proposes a more sophisticated version of Fearnhead's proposal. * H.F. Lopes & R. S. Tsay, Particle Filters and Bayesian Inference in Financial Econometrics, J. Forecasting, 2010. 
This paper reviews at length the socalled particle learning, i.e. auxiliary
particle filter with perfect adaption and MCMC moves for static
parameters, for online Bayesian parameter estimation with
detailed simulation results illustrating the degeneracy problem. A pragmatic approach consists of adding an artificial dynamic noise on the static parameter:
* J. Liu & M. West, Combined parameter and state estimation in simulationbased filtering, in SMC in Practice, 2001. Pdf  Introduce artificial dynamic noise on the static parameter and mitigate the variance inflation using a shrinkage procedure.
SMC for online and batch maximum likelihood inference of static
parameter estimation in statespace models
As
all the SMC procedures for online Bayesian inference suffer from the
degeneracy problem, me and my colleagues have tried for many years to
develop alternative methods which bypass this problem. If you accept to
be nonBayesian about the parameter, this is possible. An earlier
approach we considered consists of
using a pseudolikelihood, this yields an estimate which is not
statistically efficient but you do not even need particle filters in
this case. Eventually, we have come up with a
forwardonly implementation of the forward filteringbackward smoothing
procedure: it is
the key to obtain stable algorithms to perform online Maximum
Likelihood (ML) static parameter
estimation in statespace models. For
offline approaches, all the smoothing approaches described previously
can be and have been used. * C. Andrieu, A.D. & V.B. Tadic, Online EM for parameter
estimation in nonlinearnon Gaussian statespace models, Proc. IEEE
CDC, 2005 Pdf 
This paper describe a pseudolikelihood approach originally proposed by Ryden for
finite HMM, establish theoretical results, application to online
parameter estimation where pseudolikelihood is maximized using the
online EM.
*
J. Olsson, O. Cappe, R. Douc & E. Moulines, SMC Smoothing with
Application to Parameter Estimation in Nonlinear StateSpace Models, Bernoulli,
2008 Pdf  This paper quantifies the bias introduced by the fixedlag approximation of
Kitagawa & Sato, presents numerical results and uses it for parameter
estimation using offline EM.
* P. Del Moral, A.D. & S.S. Singh, Forward Smoothing using SMC, Technical report
Cambridge University TR 638, Sept. 2009, revised 2010 Pdf.
 This paper describes an SMC implementation of the forward filteringbackward
smoothing to compute expectations of additive functionals that bypasses
entirely the backward pass, presents theoretical results and applied it
to online parameter estimation using online gradient.
* S. Malik & M.K. Pitt, Particle filters for continous likelihood evaluation and maximisation. J. Econometrics,
2011 (slighly revised version of M.K. Pitt, Smoother particle filters
for likelihood evaluation and maximisation, Technical report, 2002). 
Even if you fixed the random seed in your particle filter, the
resulting simulated likelihood function is discontinuous and can vary
significantly for a moderate number of particles as the resampling step
is a discontinuous operation. Evaluating the MLE by maximizing this
function is thus difficult. For onedimensional state, M.K. Pitt
proposed a simple and efficient solution to this problem in 2002, just
reorder the particles on the real line and perform a piecewise linear
approximation of the resulting empirical CDF. You obtain a continuous simulated likelihood function.
* G. Poyiadjis, A.D. & S.S. Singh, Particle Approximations of the Score and
Observed Information Matrix in StateSpace Models with Application to
Parameter Estimation, Biometrika, 2011 Pdf 
This paper describes an original approach to compute the score vector and OIM online that
is more robust than the standard approach, it can be interpreted as a
particular case of the forward smoothing and we show how it can be used for online ML parameter
estimation.
*
J. Olsson & J. Westerborn, Efficient particlebased online
smoothing in general hidden Markov models: the PaRIS algorithm, Bernoulli, 2016 to appear. Pdf 
This paper shows that you can bypass the O(N^2) computational
complexity at each time step of the forwardonly procedures in (Del
Moral, D. & Singh, 2009; Poyadjis, D. & Singh, 2011) but using
a further Monte Carlo approximation based on rejection sampling.
* E.L. Ionides, A. Bhadra, Y. Atchade & A. King, Iterated Filtering, Annals of Statistics, 2011. Pdf 
This paper shows how can one can compute an approximation of the score vector using
a perturbed statespace model and apply it to batch ML parameter
estimation. It is especially useful in scenarios where one does not
have access to the expression of the transition kernel of the latent
process.
*
E.L. Ionides, D. Nguyen, Y. Atchade, S. Stoev and A.A. King, Inference
for dynamic and latent variable models via iterated, perturbed Bayes
maps, PNAS, 2015.  An elegant iterative algorithm to perform batch ML iteration.
SMC for batch Bayesian static parameter estimation in statespace models
Obviously
for batch joint Bayesian state and parameter estimation, you can use
MCMC methods. However it can be difficult to design efficient
algorithms. In the context where one can only simulate the latent
process but does not have access to the transition prior, standard MCMC
just fail. SMC can come to the rescue in these scenarios.
* Lee, D.S. & Chia, N.K.K, A particle algorithm for sequential Bayesian parameter estimation and model selection, IEEE Trans. Signal Proc., 2002. 
This paper proposes to use particle filters mixed with MCMC steps. MCMC steps are
used to rejuvenate the whole state sequence and parameter so this is
not an online algorithm as the complexity increases over time but can
be used as an alternative to standard MCMC.
* C. Andrieu, A.D. & R. Holenstein, Particle Markov chain Monte Carlo for Efficient Numerical Simulation, in Monte Carlo and Quasi Monte Carlo Methods 2008, Lecture Notes in Statistics, Springer, pp. 4560, 2009. Pdf and Particle Markov chain Monte Carlo methods (with discussion), JRSS B, 2010 Pdf 
This paper shows that it is possible to build highdimensional proposal
distributions for MCMC using SMC, it can be used to develop algorithms
to sample from the joint posterior distribution of states
and parameters. NB: At the 101 level, you can establish the validity of
the particle marginal MetropolisHastings on the parameter component from
the unbiasedness of the marginal likelihood estimator. As pointed out
in Section 5.1. of the paper, this is somewhat restrictive and
misleading. In my opinion,
the nicest part of the paper is the conditional SMC/particle Gibbs
sampler bit which does not follow at all from the unbiasedness of
the marginal likelihood estimator.
* N. Whiteley, C. Andrieu & A.D., Efficient
Bayesian Inference for
Switching StateSpace Models using Discrete Particle Markov Chain
Monte Carlo methods, Technical report no. 1004 Department of Mathematics Bristol
University 2010 Pdf 
This paper shows how the discrete particle filter of Fearnhead and
Clifford (2003)
can be used within MCMC, also presents an original backward sampling
procedure in a nonMarkovian framework which is an extension of the
procedure
originally proposed by Whiteley in the discussion of the particle MCMC
paper.
* N. Chopin, P. Jacob &
O. Papaspiliopoulos. SMC^2: A SMC algorithm with particle MCMC
updates. JRSS B, 2013 Pdf 
This paper substitutes to the MCMC used in the particle MCMC paper an SMC
algorithm, you obtain a hierarchical SMC algorithm. This yields a
powerful algorithm for sequential inference; this is not a truly online
algorithm as
the complexity increases over time.
* A. Fulop & J. Li, Robust and Efficient Learning: A Marginalized ResampleMove Approach, J. Econometrics, 2013. Pdf  The authors proposed independently the same algorithm as SMC^2. * F. Lindsten, M.I. Jordan and T.B. Schon, Particle Gibbs with Ancestor Sampling, JMLR, 2014. Pdf  Proposes an interesting variant of the particle Gibbs sampler where ancestors are resampled in a forward pass.
* A.D., M.K. Pitt, G. Deligiannidis and R. Kohn, Efficient
Implementation of Markov chain Monte Carlo when Using an Unbiased
Likelihood, Biometrika, 2015. Pdf  When implementing the particle MetropolisHastings
algorithm, and generally speaking any pseudomarginal algorithm, how
many particles one should use to evaluate the likelihood so as to
minimise the asymptotic variance of the resulting MCMC estimates for a
fixed computational budget? This paper provides useful guidelines. * N. Chopin & S.S. Singh. On the particle Gibbs sampler, Bernoulli, 2015 Pdf 
Establishes uniform ergodicity of the particle Gibbs sampler through a
coupling argument and discusses some algorithmic variants.
* S.S. Singh, F. Lindsten & E. Moulines, Blocking strategies and stability of particle Gibbs samplers, Preprint 2015 Pdf 
Assume you want to sample the posterior distribution of a very
long hidden state sequence. This paper shows that you can use a
blocking version of particle Gibbs such that the cost
periteration only grows linearly
with the number of latent states while the mixing rate of the sampler
does not deteriorate. Additionally the algorithm is easily
parallelizable. * N. Whiteley and A. Lee, Twisted particle filters, Annals of Statistics, 2014 Pdf 
Introduces a new class of particle filters which can provide
significantly lower variance estimates of the normalizing constant at
the cost of a simple algorithmic modification. It relies on a change of measure on the particle system. SMC as an alternative/complement to MCMC
The
idea of mixing SMC and MCMC to sample from a sequence of distributions
all defined on the same space has appeared independently in various
papers. * G.E. Crooks, Nonequilibrium Measurements of Free Energy Differences for Microscopically Reversible Markovian Systems, J. Stat. Phys., 1998. Pdf 
This paper presents a discretetime version of the celebrated Jarzynski's equality in
statistical physics (and slight generalization of it) showing that a
sequence of nonhomogeneous MCMC kernels "moving" towards a target can
be reweighted using a
clever importance sampling trick based on some reverse Markov kernels to obtain an unbiased estimator of
the normalizing constant and an approximation of the target of
interest. Not quite SMC as no resampling is used.
* W. Gilks & C. Berzuini, Following a Moving Target: Monte Carlo Inference for Dynamic Bayesian Models, JRSS B,
2001 
This paper proposes to move the particles using MCMC moves with the
appropriate invariant distribution so as to introduce diversity among
particles. It was discussed in the framework of statespace models with
static parameters. If you don't include any state, this gives you a
method for sampling the sequence of posteriors of the parameter. It
also introduces independently the same construction as Crooks based
on a sequence of reverse Markov kernels (this is hidden in the
proof; see Lemma 1 and Corollary 1). * R.M. Neal, Annealed Importance Sampling, Stat. Comp., 2001. Pdf  This paper introduced independently a discretetime version of the celebrated Jarzynski's equality in
statistical physics based on a sequence of reverse Markov kernels and discusses
carefully some applications to Bayesian inference with a sequence of annealed target distributions.
Not quite SMC as no resampling is used. In this context, increasing the
number of annealed auxiliary target distributions can prevent standard
weights degeneracy.
* N. Chopin, A Sequential Particle Filter Method for Static Models, Biometrika, 2002. 
This paper details a careful application of the resample move algorithm for
sampling from the sequence of posterior distributions of a static
parameter.
* P. Del Moral, A.D. & A. Jasra, Sequential Monte Carlo Samplers, JRSS B, 2006. Pdf  This paper discusses a framework generalizing annealed importance sampling and
resamplemove, discusses the potential benefits of resampling when the
sequence of targets evolve quickly and scenarios where previous methods are not applicable.
Recent Convergence Results of Interest
Besides
Del Moral's books (2004, 2013), here are a few papers of interest
essentially weakening the assumptions in the aforementioned books.
* N. Whiteley, Stability properties of particle filters, Annals Applied Proba, 2013. Pdf  Weak(er) assumptions ensuring uniform convergence of particle filters, requires bounded observations.
* R. Douc, E. Moulines & J. Olsson, Longterm stability of SMC methods under verifiable conditions, Annals Applied Proba, to appear. Pdf  Other weak(er) assumptions ensuring uniform convergence of particle filters.
*
J. Berard, P. Del Moral & A.D., A lognormal central limit theorem
for particle approximations of normalizing constants, Electronic J. Proba, 2014 Pdf. 
Standard CLT for particle methods assume the time horizon T is fixed
and the number N of particles goes to infinity. However, in
practice people usually scale N linearly with T when estimating
normalizing constants/marginal likelihoods. This paper establishes a CLT for the
resulting estimate as T goes to infinity. The normalizing constant estimate divided
by the true normalizing constant converges towards a lognormal
distribution.
What about SMC in high dimensional spaces?
* R. Van Handel, Can particle filters beat the curse of dimensionality? Pdf  Answer is yes if you are ready to accept some bias but current assumptions to ensure this are extremely strong, or no bias but the rate of convergence is slowler than the usual Monte Carlo rate.
* A. Beskos, D. Crisan & A. Jasra, On the stability of SMC in high dimensions. Annals Applied Proba. Pdf 
Look at stability of SMC samplers in high dimension for i.i.d targets.
Things are not going exponentially bad but only quadratically with the dimension if your MCMC
kernels mix well. Slides and Videolectures
* Video of my talk at French Statistical Meeting on particle MCMC (in french).
* Slides of my 8 hours
course Statistical Mathematics summer school 2011 (to be posted). This is a much
updated version of a course I gave in SAMSI in Fall 2008.
* Slides of my course at Machine Learning Summer School 2012
* Slides of P. Fearnhead for a graduate course in computational statistics 2012
Code
* LibBi
by L. Murray (Oxford): powerful C++ template library + parser & compiler in Perl to perform particle
filters, PMCMC and SMC^2 at lightspeed, works for multicore CPU and
GPU too.
* Probabilistic C by B. Paiges & F. Wood (Oxford): compilationtarget for probabilistic programming languages, includes SMC and PMCMC.
* Anglican by F. Wood et al. (Oxford): open
source, compiled probabilistic programming language, includes PMCMC.
* A.M. Johansen: C++ Sequential Monte Carlo template link and an R interface to this package by D. Eddelbuettel link
* A. King, E.L. Ionides et al.: R package
Statistical inference for partially observed models, includes bootstrap
filter, iterated filtering and particle Marginal MetropolisHastings.
* A. Lee et al.: GPU code for particle filters and SMC samplers link and paper.
* F. Lindsten: Matlab code for particle Marginal MetropolisHastings and particle Gibbs with ancestor sampling link
* D. Rasmussen: Matlab code for particle Marginal MetropolisHastings implemented in this 2011 PLOS Comp. Biology paper.
* T. Schon: Matlab code for RaoBlackwellised particle filter and EM for parameter estimation.
* D. Wilkinson: R code corresponding to the second edition of his cool book, includes particle Marginal MetropolisHastings (chapter 10).