Matti Vihola - Publications

Preprints

J. Karjalainen, A. Lee, S. S. Singh and M. Vihola. Mixing time of the conditional backward sampling particle filter.
arXiv:2312.17572
J. Karjalainen, A. Lee, S. S. Singh and M. Vihola. On the forgetting of particle filters.
arXiv:2309.08517
A. Durmus, S. Gruffaz, M. Kailas, E. Saksman and M. Vihola. On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers.
arXiv:2307.03460
J. Karvanen, S. Tikka and M. Vihola. Simulating counterfactuals.
arXiv:2306.15328

Journal articles

S. Karppinen, S. S. Singh and M. Vihola. Conditional particle filters with bridge backward sampling. Journal of Computational and Graphical Statistics, to appear. doi:10.1080/10618600.2023.2231514
Preprint: arXiv:2205.13898
The Julia codes
N. Chopin, S. S. Singh, T. Soto and M. Vihola. On resampling schemes for particle filters with weakly informative observations. Annals of Statistics, 50(6):3197-3222, 2022. doi:10.1214/22-AOS2222
Preprint: arXiv:2203.10037
The Julia codes; see also the Resamplings.jl package
Presentation at SMC 2022
S. Karppinen, T. Rajala, S. Mäntyniemi, I. Kojola, and M. Vihola. Identifying territories using presence-only citizen science data: an application to the Finnish wolf population. Ecological Modelling, 472, 2022. doi:10.1016/j.ecolmodel.2022.110101
The Julia codes
M. Kuronen, A. Särkkä, M. Vihola and M. Myllymäki. Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests. Environmental and Ecological Statistics, 29, 185–205, 2022. doi:10.1007/s10651-021-00514-3
Preprint: arXiv:2005.01962
J. Helske and M. Vihola. bssm: Bayesian Inference of Non-linear and Non-Gaussian State Space Models in R. The R Journal, 13(2), 471–484, 2021. doi:10.32614/RJ-2021-103.
Preprint: arXiv:2101.08492
The bssm package
N. Chada, J. Franks, A. Jasra, K. Law and M. Vihola. Unbiased inference for discretely observed hidden Markov model diffusions. SIAM/ASA Journal on Uncertainty Quantification, 9(2), 763–787, 2021. doi:10.1137/20M131549X
Preprint: arXiv:1807.10259
S. Karppinen and M. Vihola. Conditional particle filters with diffuse initial distributions. Statistics and Computing, 31(3), article 24, 2021. doi:10.1007/s11222-020-09975-1
Preprint: arXiv:2006.14877
Julia codes
Presentation at MCQMC 2020
M. Vihola, J. Helske and J. Franks. Importance sampling type estimators based on approximate marginal MCMC. Scandinavian Journal of Statistics, 47(4), 1339–1376, 2020. doi:10.1111/sjos.12492
Preprint: arXiv:1609.02541
The related bssm R package
Presentation at IMS workshop
J. Franks and M. Vihola. Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance. Stochastic Processes and Their Applications, 130(10), 6157–6183, 2020. doi:10.1016/j.spa.2020.05.006
Preprint: arXiv:1706.09873
A. Lee, S. S. Singh and M. Vihola. Coupled conditional backward sampling particle filter. Annals of Statistics, 48(5), 3066-3089, 2020. doi:10.1214/19-AOS1922
Preprint: arXiv:1806.05852
The related Julia package
M. Vihola and J. Franks. On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction. Biometrika, 107(2), 381–395, 2020. doi:10.1093/biomet/asz078
Preprint: arXiv:1902.00412
The related Julia package, the Julia codes of the experiments
Presentation at One World ABC seminar
S. Karppinen, O. Lohi and M. Vihola. Prediction of leukocyte counts during paediatric acute lymphoblastic leukaemia maintenance therapy. Scientific Reports 9(18076), 2019. doi:10.1038/s41598-019-54492-5
C. Andrieu, A. Lee and M. Vihola. Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers. Bernoulli 24(2), 842–872, 2018. doi:10.3150/15-BEJ785
Preprint: arXiv:1312.6432
M. Vihola. Unbiased estimators and multilevel Monte Carlo. Operations Research, 66(2):448–462, 2018. doi:10.1287/opre.2017.1670
Preprint: arXiv:1512.01022
The related software
Presentation at ESI workshop
L. Leskelä and M. Vihola. Conditional convex orders and measurable martingale couplings. Bernoulli, 23(4A):2784–2807, 2017. doi:10.3150/16-BEJ827
Preprint: arXiv:1404.0999
G. Fort, E. Moulines, A. Schreck and M. Vihola. Convergence of Markovian stochastic approximation with discontinuous dynamics. SIAM Journal on Control and Optimization. 54(2):866–893, 2016. doi:10.1137/140962723
Preprint: arXiv:1403.6803
C. Andrieu and M. Vihola. Establishing some order amongst exact approximations of MCMCs. Annals of Applied Probability. 26(5):2661–2696, 2016. doi:10.1214/15-AAP1158
Preprint: arXiv:1404.6909
I. S. Mbalawata, S. Särkkä, M. Vihola and H. Haario. Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter. Computational Statistics & Data Analysis, 83(3):101–115, 2015. doi:10.1016/j.csda.2014.10.006
Preprint: arXiv:1308.5875
C. Andrieu, G. Fort and M. Vihola. Quantitative convergence rates for sub-geometric Markov chains. Journal of Applied Probability, 52(2):391–404, 2015. doi:10.1239/jap/1437658605
Preprint: arXiv:1309.0622
C. Andrieu and M. Vihola. Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms. Annals of Applied Probability, 25(2):1030–1077, 2015. doi:10.1214/14-AAP1022
Preprint: arXiv:1210.1484
C. Andrieu, V. B. Tadic and M. Vihola. On the stability of some controlled Markov chains and its applications to stochastic approximation with Markovian dynamic. Annals of Applied Probability 25(1):1–45, 2015. doi:10.1214/13-AAP953
Preprint: arXiv:1205.4181
C. Andrieu and M. Vihola. Markovian stochastic approximation with expanding projections. Bernoulli, 20(2):545–585, 2014. doi:10.3150/12-BEJ497
Preprint: arXiv:1111.5421
B. Miasojedow, E. Moulines and M. Vihola. An adaptive parallel tempering algorithm. Journal of Computational and Graphical Statistics 22(3):649–664, 2013. doi:10.1080/10618600.2013.778779
Preprint: arXiv:1205.1076
The Matlab-codes of the experiments; Julia implementation in the AdaptiveMCMC.jl package
L. Leskelä and M. Vihola. Stochastic order characterization of uniform integrability and tightness. Statistics and Probability Letters 83(1):382–389, 2013. doi:10.1016/j.spl.2012.09.023
Preprint: arXiv:1106.0607
M. Vihola Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing 22(5):997–1008, 2012. doi:10.1007/s11222-011-9269-5
Preprint: arXiv:1011.4381
A Python implementation; a Matlab implementation; Julia implementation in AdaptiveMCMC.jl; C implementation in Grapham
M. Vihola. Can the adaptive Metropolis algorithm collapse without the covariance lower bound?. Electronic Journal of Probability 16:45–75, 2011. doi:10.1214/EJP.v16-840
Preprint: arXiv:0911.0522
M. Vihola. On the stability and ergodicity of adaptive scaling Metropolis algorithms. Stochastic Processes and Their Applications 121(12):2839–2860, 2011. doi:10.1016/j.spa.2011.08.006
Preprint: arXiv:0903.4061
M. Vihola. Grapham: Graphical models with adaptive random walk Metropolis algorithms. Computational Statistics & Data Analysis 54(1):49–54, 2010. doi:10.1016/j.csda.2009.09.001
Preprint: arXiv:0811.4095
The related software
E. Saksman and M. Vihola. On the ergodicity of the adaptive Metropolis algorithm on unbounded domains. Annals of Applied Probability 20(6):2178–2203, 2010. doi:10.1214/10-AAP682
Preprint: arXiv:0806.2933
M. Vihola. Rao-Blackwellised particle filtering in random set multitarget tracking. IEEE Transactions on Aerospace and Electronic Systems 43(2):689–705, 2007. doi:10.1109/TAES.2007.4285362
Final draft, post-refereeing: PS.gz/PDF

Book chapters

M. Vihola. Bayesian inference with Adaptive Markov chain Monte Carlo. In Computational Statistics in Data Science, Piegorsch, W.W., Levine, R.A., Zhang, H.H., and Lee, T.C.M. (eds.), Chichester: John Wiley & Sons, ISBN: 978-1-119-56107-1, 2022.

Electronic version of the above has been published earlier under the following title:
M. Vihola. Ergonomic and reliable Bayesian inference with adaptive Markov chain Monte Carlo. In Wiley StatsRef: Statistics Reference Online. Davidian, M., Kenett, R.S., Longford, N.T., Molenberghs, G., Piegorsch, W.W., and Ruggeri, F. (eds.), Article No. stat08286, 2020.
doi:10.1002/9781118445112.stat08286
The related Julia packages AdaptiveMCMC.jl and AdaptiveParticleMCMC.jl
C. Andrieu, A. Lee and M. Vihola. Theoretical and methodological aspects of MCMC computations with noisy likelihoods. in S. A. Sisson, Y. Fan and M. Beaumont (eds.) Handbook of Approximate Bayesian Computation, Chapman and Hall/CRC, 2018. doi:10.1201/9781315117195
Final draft available in JYX Digital Repository

In conference proceedings

F. Lindsten, J. Helske and M. Vihola. Graphical model inference: Sequential Monte Carlo meets deterministic approximations. Advances in Neural Information Processing Systems 31, pp. 8200–8210, 2018. (link to paper)
Preprint: arXiv:1901.02374
M. Vihola. Random set particle filter for bearings-only multitarget tracking. Signal Processing, Sensor Fusion, and Target Recognition XIV, ed. Ivan Kadar, Proc. SPIE, Vol. 5809, pp. 301–312, 2005. doi:10.1117/12.602682
PS.gz/PDF
M. Vihola, M. Harju, P. Salmela, J. Suontausta, and J. Savela. Two dissimilarity measures for HMMs and their application in phoneme model clustering. Proc. ICASSP 2002, Vol. I, pp. 933–936, 2002. doi:10.1109/ICASSP.2002.5743946
PDF

Theses

On the convergence of unconstrained adaptive Markov chain Monte Carlo algorithms. PhD thesis, University of Jyväskylä, Department of Mathematics and Statistics, Report 124, Jyväskylä 2010, ISSN: 1457-8905, ISBN: 978-951-39-3809-3.
Introduction: PDF
Awarded with the Rolf Nevanlinna doctoral thesis award
Random sets for multitarget tracking and data fusion. Licentiate thesis, Tampere University of Technology, Oct. 2004.
PS.gz/PDF/linked PDF
Dissimilarity measures for hidden Markov models and their application in multilingual speech recognition. MSc thesis, Tampere University of Technology, May 2002.
PS.gz/PDF